{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FashionNet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "出现的问题汇总：\n",
    "1. ROI pooling自定义层难点：\n",
    "\n",
    "    faster rcnn中的该层使用的都是单张图片输入，每张图片读取一个多个ROI，一次只读取一张图片。但是fashionNet需要每张图片读取8个固定的ROI，且一次读取多张图片\n",
    "\n",
    "    解决办法：在ROI中使用`batches = K.shape(box)[0];box_ind = K.arange(batches)`获取batch大小，从而实现读取多张图片，但似乎会导致内存溢出。目前好的解决办法\n",
    "    \n",
    "    \n",
    "2. 关于内存溢出（OOM）的一种解决办法：\n",
    "\n",
    "    fashionnet引入了triplet loss来控制类之间的距离，但是一次需要同时使用三张图片输入，加上模型为深层网络，容易造成内存溢出（当前使用batches=1也不行）。\n",
    "    \n",
    "    解决办法：可以尝试拆除模型triplet loss。当前版本已经拆除triplet loss。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## dependency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-12T00:46:24.653589Z",
     "start_time": "2019-04-12T00:46:21.990885Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# The Code reproduced from the Fashion Net paper\n",
    "# \n",
    "# Author: Jasonsey\n",
    "# Email: 2627866800@qq.com\n",
    "# \n",
    "# =============================================================================\n",
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '2'  # which gpu to use\n",
    "from pathlib import Path\n",
    "import six\n",
    "import math\n",
    "import itertools\n",
    "from multiprocessing.pool import Pool\n",
    "\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras import layers, backend, models, utils\n",
    "from keras.engine.topology import Layer\n",
    "from keras import backend as K\n",
    "from keras.models import Model, Sequential\n",
    "from keras_contrib.applications import ResNet\n",
    "from keras.optimizers import Adam\n",
    "from keras.utils import Sequence, to_categorical\n",
    "from keras.preprocessing.image import img_to_array, array_to_img\n",
    "from keras.callbacks import ModelCheckpoint, CSVLogger, TensorBoard, LambdaCallback\n",
    "from keras.applications.resnet50 import preprocess_input\n",
    "import keras_applications\n",
    "from keras_applications.resnet import ResNet50, ResNet101, ResNet152\n",
    "from keras.applications import VGG16\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image, ImageFile\n",
    "from cv2 import cv2\n",
    "from lapjv import lapjv    # if use conda，need to install from lapjv's git repo\n",
    "import imgaug as ia\n",
    "from imgaug import augmenters as iaa\n",
    "from sklearn.preprocessing import MultiLabelBinarizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-12T00:46:25.140939Z",
     "start_time": "2019-04-12T00:46:24.658199Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_name</th>\n",
       "      <th>item_id</th>\n",
       "      <th>landmark_visibility_1</th>\n",
       "      <th>landmark_location_x_1</th>\n",
       "      <th>landmark_location_y_1</th>\n",
       "      <th>landmark_visibility_2</th>\n",
       "      <th>landmark_location_x_2</th>\n",
       "      <th>landmark_location_y_2</th>\n",
       "      <th>landmark_visibility_3</th>\n",
       "      <th>landmark_location_x_3</th>\n",
       "      <th>...</th>\n",
       "      <th>landmark_location_x_6</th>\n",
       "      <th>landmark_location_y_6</th>\n",
       "      <th>landmark_visibility_7</th>\n",
       "      <th>landmark_location_x_7</th>\n",
       "      <th>landmark_location_y_7</th>\n",
       "      <th>landmark_visibility_8</th>\n",
       "      <th>landmark_location_x_8</th>\n",
       "      <th>landmark_location_y_8</th>\n",
       "      <th>attribute_labels</th>\n",
       "      <th>category_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>img/WOMEN/Dresses/id_00000002/02_1_front.jpg</td>\n",
       "      <td>id_00000002</td>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>...</td>\n",
       "      <td>161.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>230.0</td>\n",
       "      <td>0 2 5 6 9 11 13 14 15 22 26 52 85 92 143 161 1...</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>img/WOMEN/Dresses/id_00000002/02_2_side.jpg</td>\n",
       "      <td>id_00000002</td>\n",
       "      <td>1</td>\n",
       "      <td>127</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
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       "      <td>61</td>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
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       "      <td>125.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>0 2 5 6 9 11 13 14 15 22 26 52 85 92 143 161 1...</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>img/WOMEN/Dresses/id_00000002/02_4_full.jpg</td>\n",
       "      <td>id_00000002</td>\n",
       "      <td>0</td>\n",
       "      <td>123</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "      <td>...</td>\n",
       "      <td>149.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0 2 5 6 9 11 13 14 15 22 26 52 85 92 143 161 1...</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>img/WOMEN/Dresses/id_00000002/02_7_additional.jpg</td>\n",
       "      <td>id_00000002</td>\n",
       "      <td>0</td>\n",
       "      <td>153</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>112</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>...</td>\n",
       "      <td>108.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>0 2 5 6 9 11 13 14 15 22 26 52 85 92 143 161 1...</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>img/WOMEN/Skirts/id_00000003/02_1_front.jpg</td>\n",
       "      <td>id_00000003</td>\n",
       "      <td>1</td>\n",
       "      <td>79</td>\n",
       "      <td>131</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>130</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0 1 2 6 12 13 20 23 42 55 84 87 113 152 171 19...</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          image_name      item_id  \\\n",
       "0       img/WOMEN/Dresses/id_00000002/02_1_front.jpg  id_00000002   \n",
       "1        img/WOMEN/Dresses/id_00000002/02_2_side.jpg  id_00000002   \n",
       "2        img/WOMEN/Dresses/id_00000002/02_4_full.jpg  id_00000002   \n",
       "3  img/WOMEN/Dresses/id_00000002/02_7_additional.jpg  id_00000002   \n",
       "4        img/WOMEN/Skirts/id_00000003/02_1_front.jpg  id_00000003   \n",
       "\n",
       "   landmark_visibility_1  landmark_location_x_1  landmark_location_y_1  \\\n",
       "0                      1                    109                     63   \n",
       "1                      1                    127                     59   \n",
       "2                      0                    123                     46   \n",
       "3                      0                    153                     58   \n",
       "4                      1                     79                    131   \n",
       "\n",
       "   landmark_visibility_2  landmark_location_x_2  landmark_location_y_2  \\\n",
       "0                      0                    156                     70   \n",
       "1                      0                    145                     61   \n",
       "2                      0                    148                     51   \n",
       "3                      0                    112                     61   \n",
       "4                      1                    125                    130   \n",
       "\n",
       "   landmark_visibility_3  landmark_location_x_3  ...  landmark_location_x_6  \\\n",
       "0                      0                     94  ...                  161.0   \n",
       "1                      1                    121  ...                  141.0   \n",
       "2                      0                    108  ...                  149.0   \n",
       "3                      0                    172  ...                  108.0   \n",
       "4                      0                     67  ...                    0.0   \n",
       "\n",
       "   landmark_location_y_6  landmark_visibility_7  landmark_location_x_7  \\\n",
       "0                  136.0                    0.0                   89.0   \n",
       "1                  125.0                    1.0                  123.0   \n",
       "2                   96.0                    0.0                  101.0   \n",
       "3                  141.0                    0.0                  175.0   \n",
       "4                    0.0                    0.0                    0.0   \n",
       "\n",
       "   landmark_location_y_7  landmark_visibility_8  landmark_location_x_8  \\\n",
       "0                  234.0                    0.0                  206.0   \n",
       "1                  217.0                    0.0                  132.0   \n",
       "2                  149.0                    0.0                  157.0   \n",
       "3                  228.0                    0.0                   91.0   \n",
       "4                    0.0                    0.0                    0.0   \n",
       "\n",
       "   landmark_location_y_8                                   attribute_labels  \\\n",
       "0                  230.0  0 2 5 6 9 11 13 14 15 22 26 52 85 92 143 161 1...   \n",
       "1                  229.0  0 2 5 6 9 11 13 14 15 22 26 52 85 92 143 161 1...   \n",
       "2                  156.0  0 2 5 6 9 11 13 14 15 22 26 52 85 92 143 161 1...   \n",
       "3                  233.0  0 2 5 6 9 11 13 14 15 22 26 52 85 92 143 161 1...   \n",
       "4                    0.0  0 1 2 6 12 13 20 23 42 55 84 87 113 152 171 19...   \n",
       "\n",
       "   category_label  \n",
       "0              12  \n",
       "1              12  \n",
       "2              12  \n",
       "3              12  \n",
       "4              19  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_dict = {\n",
    "    1: 'fashionnet',\n",
    "    2: 'resnet34'\n",
    "}\n",
    "no_model = 1\n",
    "\n",
    "\n",
    "TRAIN_DF = '../data/input/train.csv'\n",
    "VAL_DF = '../data/input/val.csv'\n",
    "TEST_DF = '../data/input/test.csv'\n",
    "\n",
    "MODEL_PATH = f'../data/output/models/{model_dict[no_model]}/'\n",
    "LOGGER_PATH = f'../data/output/logs/{model_dict[no_model]}/'\n",
    "\n",
    "\n",
    "train_df = pd.read_csv(TRAIN_DF).fillna(0)\n",
    "val_df = pd.read_csv(VAL_DF).fillna(0)\n",
    "test_df = pd.read_csv(TEST_DF).fillna(0)\n",
    "\n",
    "\n",
    "image_shape = (224, 224, 3)  # all images will be adjusted to this shape\n",
    "num_category = 23  # all images belong to 23 category\n",
    "num_attr = 463  # each image has 463 attribute \n",
    "num_landmark_visibility = 3  # landmark has 3 status: visible, unvisible, unsure\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-12T00:46:25.163000Z",
     "start_time": "2019-04-12T00:46:25.144016Z"
    }
   },
   "outputs": [],
   "source": [
    "def expand_path(p, **kwargs):\n",
    "    \"\"\"read the complete path\n",
    "    \n",
    "    Arguments\n",
    "        p: the string of path that save in csv files, such as `img/MEN/Denim/id_00000080/01_1_front.jpg`\n",
    "        \n",
    "    Return:\n",
    "        the complete path, such as `../data/input/Img/img/MEN/Denim/id_00000080/01_1_front.jpg`\n",
    "    \"\"\"\n",
    "    header = Path('../data/input/Img/')\n",
    "    return str(header / p)\n",
    "\n",
    "\n",
    "def read_raw_image(p, **kwargs):\n",
    "    \"\"\"read image from disk\n",
    "    \n",
    "    Arguments\n",
    "        p: the string of path that save in csv files, such as `img/MEN/Denim/id_00000080/01_1_front.jpg`\n",
    "    \n",
    "    Return\n",
    "        The pil image object\n",
    "    \"\"\"\n",
    "    img = Image.open(expand_path(p))\n",
    "    return img\n",
    "\n",
    "\n",
    "def show_img(imgs, per_row=2, rescale=False):\n",
    "    \"\"\"show images in cell's output\n",
    "    \n",
    "    Arguments\n",
    "        imgs: a list of np.ndarray or pil.Image.Image, the length of imgs should no less than 2\n",
    "        per_row: how manny images are showed per row\n",
    "        rescale: if the images have been rescale into 0~1, then the rescale should be True\n",
    "    \n",
    "    Return\n",
    "        None       \n",
    "    \"\"\"\n",
    "    images = []\n",
    "    for i in range(len(imgs)):\n",
    "        if isinstance(imgs[i], np.ndarray):\n",
    "            if rescale:\n",
    "                mm, mn = imgs[i].max(), imgs[i].min()\n",
    "                scale = 255 / (mm-mn)\n",
    "                imgs[i] += mn\n",
    "                imgs[i] *= scale\n",
    "            images.append(Image.fromarray(np.uint8(imgs[i])))\n",
    "        else:\n",
    "            images.append(imgs[i])\n",
    "    imgs = images\n",
    "    n = len(imgs)\n",
    "    rows = (n + per_row - 1) // per_row\n",
    "    cols = min(per_row, n)\n",
    "    fig, axes = plt.subplots(rows, cols, figsize=(24 // per_row * cols, 24 // per_row * rows))\n",
    "    for ax in axes.flatten(): ax.axis('off')\n",
    "    for i, (img, ax) in enumerate(zip(imgs, axes.flatten())): ax.imshow(img.convert('RGB'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-12T00:46:25.606919Z",
     "start_time": "2019-04-12T00:46:25.166988Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show 3 of the images in the train_df\n",
    "imgs = []\n",
    "for k in train_df['image_name']:\n",
    "    im = read_raw_image(k)\n",
    "    imgs.append(im)\n",
    "    if len(imgs) == 3:\n",
    "        break\n",
    "        \n",
    "show_img(imgs, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-12T00:46:25.893178Z",
     "start_time": "2019-04-12T00:46:25.610241Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show the category distribution\n",
    "_ = plt.hist(train_df['category_label'], bins=num_category)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataAgu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-12T00:46:25.905949Z",
     "start_time": "2019-04-12T00:46:25.895240Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_aug(rate=0.8):\n",
    "    \"\"\"data set augmentation tool.\n",
    "    \n",
    "    The official documents: https://github.com/aleju/imgaug\n",
    "    \n",
    "    Arguments\n",
    "        rate: the rate to use data set augmentation tool. if 1, all of the images will use data \n",
    "            set augmentation tool, if 0, none of the images will use data set augmentation tool\n",
    "    \n",
    "    Return\n",
    "        The configured data set augmentation tool\n",
    "    \"\"\"\n",
    "    sometimes = lambda aug: iaa.Sometimes(rate, aug)\n",
    "    seq = iaa.Sequential([\n",
    "        sometimes([\n",
    "            iaa.Fliplr(0.5),  # 水平翻转\n",
    "            iaa.Flipud(0.5),  # 垂直翻转\n",
    "            iaa.Crop(percent=(0, 0.15)),  # 随机剪切像素，并保证shape不变\n",
    "            iaa.Sometimes(0.5, iaa.GaussianBlur(sigma=(0, 0.05))),  # 高斯模糊\n",
    "            iaa.ContrastNormalization((0.75, 1.5)),  # 减弱或加强对比度\n",
    "            iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05*255), per_channel=0.5),  # 高斯噪点\n",
    "            iaa.Multiply((0.9, 1.1), per_channel=0.2),  # 随机变换色调\n",
    "            iaa.Sometimes(0.6, iaa.Affine(\n",
    "                scale={\"x\": (0.9, 1.1), \"y\": (0.8, 1.2)},\n",
    "                translate_percent={\"x\": (-0.1, 0.1), \"y\": (-0.1, 0.1)},\n",
    "                rotate=(-15, 15),\n",
    "                shear=(-8, 8)\n",
    "            ))\n",
    "        ]),\n",
    "        iaa.Resize({'height': image_shape[0], 'width': image_shape[1]})\n",
    "    ], random_order=True)\n",
    "    return seq\n",
    "\n",
    "\n",
    "def normal_image(img):\n",
    "    \"\"\"normalize an image by the Image Net's official preprocessing tool. \n",
    "    \n",
    "    Arguments\n",
    "        img: np.ndarray of pil.Image.Image\n",
    "        \n",
    "    Return\n",
    "        the normalized image\n",
    "    \"\"\"\n",
    "    if isinstance(img, Image.Image):\n",
    "        img = np.array(img, K.floatx())\n",
    "    img = preprocess_input(img)\n",
    "    return img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TrainSequence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "训练集生成器，保证图片在训练过程中的供给"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-12T00:46:25.926859Z",
     "start_time": "2019-04-12T00:46:25.907908Z"
    }
   },
   "outputs": [],
   "source": [
    "class TrainSequence(Sequence):\n",
    "    \"\"\"the train sequence that inherit from `Seqence` \n",
    "    \n",
    "    Arguments\n",
    "        data: a pd.DataFrame of train_df, val_df or test_df\n",
    "        batch_size：the batch size to fit the model\n",
    "    \"\"\"\n",
    "    def __init__(self, data: pd.DataFrame, batch_size=32):\n",
    "        \"\"\"耗时：1m 22s\"\"\"\n",
    "        super().__init__()\n",
    "        self.batch_size = batch_size\n",
    "        \n",
    "        self.data =data\n",
    "        self.seq = get_aug(0.8)\n",
    "        \n",
    "        self.mlb = MultiLabelBinarizer()\n",
    "        self.mlb.classes_ = list(range(num_attr))  # 总共有463个属性\n",
    "        self.on_epoch_end()\n",
    "        \n",
    "    def __getitem__(self, index):\n",
    "        \"\"\"耗时：265ms\"\"\"\n",
    "        start = self.batch_size * index\n",
    "        end = self.batch_size * (index + 1)-1\n",
    "        batches = self.data.loc[start:end]\n",
    "        seq = self.seq.to_deterministic()\n",
    "        \n",
    "        # 读取源数据\n",
    "        xs_a, attrs, cates, lands_v, lands_local = [], [], [], [], []\n",
    "        for i in range(len(batches)):\n",
    "            # read image\n",
    "            a = batches.iloc[i]\n",
    "            x_a = normal_image(np.asarray(read_raw_image(a['image_name']).convert('RGB')))\n",
    "            xs_a.append(x_a)\n",
    "            \n",
    "            # read attr, when a['attribute_labels'] has single label, it will be read as an int\n",
    "            attr = [int(at) for at in str(a['attribute_labels']).split(' ') if at != '']\n",
    "            attrs.append(attr)\n",
    "            # read category\n",
    "            cates.append(a['category_label'])\n",
    "            # read landmark\n",
    "            lands_v.extend([a[f'landmark_visibility_{ii}'] for ii in range(1, 9)])\n",
    "            lands_local.append(ia.KeypointsOnImage([\n",
    "                ia.Keypoint(\n",
    "                    x=a[f'landmark_location_x_{ii}'],\n",
    "                    y=a[f'landmark_location_y_{ii}']\n",
    "                ) for ii in range(1, 9)\n",
    "            ], shape=x_a.shape))\n",
    "            \n",
    "        # aug image\n",
    "        xs_a = np.array(seq.augment_images(xs_a))\n",
    "        # aug landmark   \n",
    "        lands_v = to_categorical(lands_v, num_landmark_visibility)\n",
    "        lands_local = seq.augment_keypoints(lands_local)\n",
    "        res = []\n",
    "        for local in lands_local:\n",
    "            for keypoint in local.keypoints:\n",
    "                res.append([keypoint.x, keypoint.y])\n",
    "        lands_local = res\n",
    "        try:\n",
    "            lands_local = np.array(lands_local) / image_shape[:2]  # 缩放比例到0-1\n",
    "        except ValueError as e:\n",
    "            # 检查过所有数据输入都不能存在空值，但是仍然出现shape不一致，这里检查那个数据异常\n",
    "            raise ValueError(f\"Error: {e}, \\n image: {batches['image_name']}'， \\n lands: {lands_local}\")\n",
    "        lands = np.concatenate((lands_v, lands_local), axis=1)\n",
    "        # from (None*8, 5) to (None, 8*5)\n",
    "        lands = lands.reshape((len(batches), -1))\n",
    "        \n",
    "        # transform category\n",
    "        cates = to_categorical(cates, num_category)\n",
    "        # transsform attrs\n",
    "        attrs = self.mlb.transform(attrs)\n",
    "\n",
    "        return xs_a, [cates, lands, cates, attrs]\n",
    "    \n",
    "    def on_epoch_end(self):\n",
    "        \"\"\"耗时：1m 3.57s\"\"\"\n",
    "        self.data = self.data.sample(frac=1.0).reset_index(drop=True)\n",
    "    \n",
    "    def __len__(self):\n",
    "        return (len(self.data) + self.batch_size - 1) // self.batch_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-12T00:46:26.081396Z",
     "start_time": "2019-04-12T00:46:25.928586Z"
    }
   },
   "outputs": [],
   "source": [
    "train = TrainSequence(data=train_df, batch_size=6)\n",
    "xs_a, [cates, lands, cates, attrs] = train[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-01T08:16:20.633845Z",
     "start_time": "2019-04-01T08:16:19.777682Z"
    },
    "scrolled": false
   },
   "source": [
    "```python\n",
    "# the code to test the TrainSequence\n",
    "train = TrainSequence(data=train_df, batch_size=6)\n",
    "xs_a, [cates, lands, cates, attrs] = train[0]\n",
    "\n",
    "lands_local = lands.reshape((-1, 5))[:, 3:].reshape((-1, 8, 2))\n",
    "imgs = []\n",
    "for i in range(len(xs_a)):\n",
    "    keypoints = ia.KeypointsOnImage([ia.Keypoint(x=x[0], y=x[1]) for x in lands_local[i]], shape=xs_a[i].shape)\n",
    "    imgs.append(keypoints.draw_on_image(xs_a[i], color=(255, 0, 0), size=7))\n",
    "show_img(imgs, 3, rescale=False); lands\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ValSequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-12T00:46:26.088579Z",
     "start_time": "2019-04-12T00:46:26.083545Z"
    }
   },
   "outputs": [],
   "source": [
    "class ValSequence(TrainSequence):\n",
    "    \"\"\"the val sequence that inherit from `TrainSeqence`\n",
    "    \n",
    "    Arguments\n",
    "        data: a pd.DataFrame of train_df, val_df or test_df\n",
    "        batch_size：the batch size to fit the model\n",
    "    \"\"\"\n",
    "    def __init__(self, data: pd.DataFrame, batch_size=32):\n",
    "        \"\"\"耗时：1m 22s\"\"\"\n",
    "        super().__init__(data=data, batch_size=batch_size)\n",
    "        # make sure donot use data set augmentation by setting rate=0\n",
    "        self.seq = get_aug(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-12T00:46:26.099449Z",
     "start_time": "2019-04-12T00:46:26.090950Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8     7319\n",
       "9     4046\n",
       "12    3464\n",
       "4     2144\n",
       "6     2010\n",
       "2     1241\n",
       "19     886\n",
       "14     871\n",
       "7      813\n",
       "17     804\n",
       "10     714\n",
       "13     672\n",
       "0      468\n",
       "3      395\n",
       "1      260\n",
       "15     215\n",
       "5       16\n",
       "Name: category_label, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['category_label'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-12T00:46:33.281002Z",
     "start_time": "2019-04-12T00:46:26.102691Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((0.04142968129714935, 0.9585703187028507),\n",
       " array([ 1.55982906,  2.80769231,  0.58823529,  1.84810127,  0.34048507,\n",
       "        45.625     ,  0.36318408,  0.89790898,  0.0997404 ,  0.18042511,\n",
       "         1.02240896,  0.        ,  0.21073903,  1.08630952,  0.83811711,\n",
       "         3.39534884,  0.        ,  0.9079602 ,  0.        ,  0.82392777,\n",
       "         0.        ,  0.        ,  0.        ]))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def category_classweight():\n",
    "    \"\"\"calculate the category's class weight\n",
    "    \n",
    "    Return\n",
    "        an 1D np.ndarray which index is equal to the category's index\n",
    "    \"\"\"\n",
    "    class_dict = train_df['category_label'].value_counts().to_dict()\n",
    "    num_max = max(class_dict.values())\n",
    "    class_weight = []\n",
    "    for i in range(num_category):\n",
    "        if i not in class_dict:\n",
    "            class_weight.append(0.)\n",
    "        else:\n",
    "            class_weight.append(730 / class_dict[i])\n",
    "    class_weight = np.array(class_weight)\n",
    "    return class_weight\n",
    "\n",
    "\n",
    "def attribute_classweight():\n",
    "    \"\"\"calculate the attribute's class weight\n",
    "    \n",
    "    Return\n",
    "        p_weight: the positive weight of the train_df\n",
    "        n_weight: the negative weight of the train_df\n",
    "    \"\"\"\n",
    "    p_weight = 0\n",
    "    for i in range(len(train_df)):\n",
    "        attrs = str(train_df.loc[i]['attribute_labels'])\n",
    "        p_weight += len(attrs.split(' '))\n",
    "    p_weight = p_weight / (len(train_df) * num_attr)\n",
    "    n_weight = 1 - p_weight\n",
    "    return p_weight, n_weight\n",
    "\n",
    "\n",
    "category_weight = category_classweight()\n",
    "attribute_weight = attribute_classweight(); attribute_weight, category_weight            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-12T00:46:33.315193Z",
     "start_time": "2019-04-12T00:46:33.285083Z"
    }
   },
   "outputs": [],
   "source": [
    "def category_loss(y_true, y_pred, gamma=0.):\n",
    "    \"\"\"the category loss using focal loss.\n",
    "    \n",
    "    The reference paper of focal loss is Focal Loss for Dense Object Detection\n",
    "    \n",
    "    Arguments\n",
    "        y_true: the learning target, shape=(None, 23)\n",
    "        y_pred: the predicted value, shape=(None, 23)\n",
    "        gamma: if 0, focal loss is equal to the category crossentropy loss.\n",
    "    \n",
    "    Return\n",
    "        the loss of shape=(None, 1)\n",
    "    \"\"\"\n",
    "    loss = -y_true * K.log(y_pred + K.epsilon()) * category_weight\n",
    "    loss = K.sum(loss, axis=-1)\n",
    "    return loss\n",
    "\n",
    "\n",
    "def attribute_loss(y_true, y_pred, gamma=0.):\n",
    "    \"\"\"the attribute loss using focal loss.\n",
    "    \n",
    "    The reference paper of focal loss is Focal Loss for Dense Object Detection\n",
    "    \n",
    "    Arguments\n",
    "        y_true: the learning target, shape=(None, 463)\n",
    "        y_pred: the predicted value, shape=(None, 463)\n",
    "        gamma: if 0, focal loss is equal to the category crossentropy loss.\n",
    "        \n",
    "    Return\n",
    "        the loss of shape=(None, 1)\n",
    "    \"\"\"\n",
    "    y_ = y_true * y_pred + (1.0 - y_true) * (1.0 - y_pred) + K.epsilon()\n",
    "    loss = -K.log(y_)\n",
    "\n",
    "    weight = y_true * attribute_weight[0] + (1.0 - y_true) * attribute_weight[1]\n",
    "    loss *= weight\n",
    "\n",
    "    loss = K.sum(loss, axis=-1)\n",
    "    return loss\n",
    "\n",
    "\n",
    "def lands_loss(y_true, y_pred):\n",
    "    \"\"\"the landmarks loss that consists of two parts, the category crossentropy of landmarks' visibility\n",
    "    and the L2 loss of landmarks' location\n",
    "    \n",
    "    Arguments\n",
    "        y_true: the learning target, shape=(None, 40). And each landmark consists of the visibility's status\n",
    "            of 3 elements and the location (x/w, y/h) of 2 elements. And each image has 8 landmarks.\n",
    "        y_pred: the predicted value, shape=(None, 40).\n",
    "        \n",
    "    Return\n",
    "        the loss of shape=(None, 1)\n",
    "    \"\"\"\n",
    "    y_true = K.reshape(y_true, (-1, 5))\n",
    "    y_pred = K.reshape(y_pred, (-1, 5))\n",
    "    \n",
    "    lands_true = y_true[:, 3:]\n",
    "    lands_pred = y_pred[:, 3:]\n",
    "    lands_gate = y_true[:, 0, None]    # only the visiable landmarks will be calculated\n",
    "    lands = K.sum((K.sigmoid(lands_pred) - lands_true) ** 2 * lands_gate, axis=-1)\n",
    "    \n",
    "    lands_v_true = y_true[:, :3]\n",
    "    lands_v_pred = y_pred[:, :3]\n",
    "    visibility = -lands_v_true * K.log(K.softmax(lands_v_pred) + K.epsilon())\n",
    "    visibility = K.sum(visibility, axis=-1)\n",
    "    \n",
    "    loss = lands + visibility\n",
    "    loss = K.sum(K.reshape(loss, (-1, 8)), axis=-1)\n",
    "    return loss\n",
    "\n",
    "\n",
    "def triplet_loss(y_true, y_pred):\n",
    "    \"\"\"the triplet loss.\n",
    "    \n",
    "    The reference paper of focal loss is In Defense of the Triplet Loss for Person Re-Identification.\n",
    "    \n",
    "    Arguments\n",
    "        y_true: the learning target, shape=(None, 1). In fact, the triplet loss dosen't need any target,\n",
    "            but the keras framework need.\n",
    "        y_pred: the predicted value, shape=(None, length of the output tensor)\n",
    "    \n",
    "    Return\n",
    "        the loss of shape=(None, 1)\n",
    "    \"\"\"\n",
    "    \n",
    "    margin = 0.3\n",
    "    loss = K.sum(y_pred, axis=-1, keepdims=True)\n",
    "    # donot use K.max, it calculates maximum value in a tensor.\n",
    "    loss = K.maximum(0., margin + loss + 0. * y_true)\n",
    "    return loss\n",
    "\n",
    "def top1(y_true, y_pred, k=1):\n",
    "    \"\"\"top1 categorical accuracy\n",
    "    \n",
    "    Arguments\n",
    "        y_true: the learning target, shape=(None, 23)\n",
    "        y_pred: the predicted value, shape=(None, 23)\n",
    "        \n",
    "    Returns\n",
    "        the top1-acc of shape=(None, 1)\n",
    "    \"\"\"\n",
    "    return K.cast(K.in_top_k(y_pred, K.argmax(y_true, axis=-1), k), K.floatx())\n",
    "\n",
    "\n",
    "def top2(y_true, y_pred, k=2):\n",
    "    \"\"\"top2 categorical accuracy\n",
    "    \n",
    "    Arguments\n",
    "        y_true: the learning target, shape=(None, 23)\n",
    "        y_pred: the predicted value, shape=(None, 23)\n",
    "        \n",
    "    Returns\n",
    "        the top2-acc of shape=(None, 1)\n",
    "    \"\"\"\n",
    "    return K.cast(K.in_top_k(y_pred, K.argmax(y_true, axis=-1), k), K.floatx())\n",
    "\n",
    "\n",
    "def top3(y_true, y_pred, k=3):\n",
    "    \"\"\"top3 categorical accuracy\n",
    "    \n",
    "    Arguments\n",
    "        y_true: the learning target, shape=(None, 23)\n",
    "        y_pred: the predicted value, shape=(None, 23)\n",
    "        \n",
    "    Returns\n",
    "        the top3-acc of shape=(None, 1)\n",
    "    \"\"\"\n",
    "    return K.cast(K.in_top_k(y_pred, K.argmax(y_true, axis=-1), k), K.floatx())\n",
    "\n",
    "\n",
    "def top5(y_true, y_pred, k=5):\n",
    "    \"\"\"top5 categorical accuracy\n",
    "    \n",
    "    Arguments\n",
    "        y_true: the learning target, shape=(None, 23)\n",
    "        y_pred: the predicted value, shape=(None, 23)\n",
    "        \n",
    "    Returns\n",
    "        the top5-acc of shape=(None, 1)\n",
    "    \"\"\"\n",
    "    return K.cast(K.in_top_k(y_pred, K.argmax(y_true, axis=-1), k), K.floatx())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Roi pooling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-12T00:46:33.344202Z",
     "start_time": "2019-04-12T00:46:33.317760Z"
    }
   },
   "outputs": [],
   "source": [
    "class RoiPooling(Layer):\n",
    "    \"\"\"Landmark ROI pooling layer for 2D inputs.\n",
    "    \n",
    "    See Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition\n",
    "    \n",
    "    Arguments\n",
    "        pool_size: int. Size of pooling region to use. pool_size = 7 will result in a 7x7 region.\n",
    "        num_rois: number of regions of interest to be used\n",
    "        window_size: the slide window size\n",
    "        \n",
    "    Input shape\n",
    "        list of one 4D tensor and one 2D tensor  [X_img,X_roi] with shape:\n",
    "        X_img: (batches, channels, rows, cols) if dim_ordering='th' or 4D tensor with shape:\n",
    "            (batch, rows, cols, channels) if dim_ordering='tf'.\n",
    "        X_roi: (batches, num_rois * 5) list of rois, with ordering (x_left_top, y_left_top) in the\n",
    "            first two elements where axis=-1.\n",
    "            \n",
    "    Output shape\n",
    "        4D tensor with shape:\n",
    "            (batches, pool_size, pool_size, channels * num_rois) if dim_ordering='tf'\n",
    "            (batch, channels * num_rois, pool_size, pool_size) if dim_ordering='th'\n",
    "    \"\"\"\n",
    "    def __init__(self, pool_size=2, num_rois=8, window_size=4, **kwargs):\n",
    "        self.dim_ordering = K.image_dim_ordering()\n",
    "        assert self.dim_ordering in {'tf', 'th'}, 'dim_ordering must be in {tf, th}'\n",
    "        self.window_size = window_size\n",
    "        self.pool_size = pool_size\n",
    "        self.num_rois = num_rois\n",
    "        self.data_format = 'channels_last' if self.dim_ordering == 'tf' else 'channels_first'\n",
    "        super().__init__(**kwargs)\n",
    "\n",
    "    def build(self, input_shape):\n",
    "        if self.dim_ordering == 'th':\n",
    "            self.nb_channels = input_shape[0][1]\n",
    "        elif self.dim_ordering == 'tf':\n",
    "            self.nb_channels = input_shape[0][3]\n",
    "\n",
    "    def compute_output_shape(self, input_shape):\n",
    "        if self.dim_ordering == 'th':\n",
    "            return None, self.num_rois * self.nb_channels, self.pool_size, self.pool_size\n",
    "        else:\n",
    "            return None, self.pool_size, self.pool_size, self.nb_channels * self.num_rois\n",
    "\n",
    "    def call(self, x, mask=None): \n",
    "        assert (len(x) == 2)\n",
    "        # 输入特征，坐标信息\n",
    "        img, rois = x[0], x[1]\n",
    "        \n",
    "        # convert img\n",
    "        if self.dim_ordering == 'th':\n",
    "            img = K.permute_dimensions(img, (0, 2, 3, 1))\n",
    "        else:\n",
    "            img = K.permute_dimensions(img, (0, 1, 2, 3))\n",
    "        \n",
    "        # convert boxes\n",
    "        shape = K.int_shape(img)\n",
    "        \n",
    "        rois = K.reshape(rois, (-1, 5))[:, 3:]\n",
    "        x1 = rois[..., 0]\n",
    "        y1 = rois[..., 1]\n",
    "        x2 = rois[..., 0] + K.cast(self.window_size / shape[2], K.floatx())\n",
    "        y2 = rois[..., 1] + K.cast(self.window_size / shape[1], K.floatx())\n",
    "        \n",
    "        x1 = K.expand_dims(x1, axis=-1)\n",
    "        y1 = K.expand_dims(y1, axis=-1)\n",
    "        x2 = K.expand_dims(x2, axis=-1)\n",
    "        y2 = K.expand_dims(y2, axis=-1)\n",
    "\n",
    "        boxes = K.concatenate([y1, x1, y2, x2], axis=-1)\n",
    "        \n",
    "        # convert img\n",
    "        img = K.concatenate([img] * self.num_rois, axis=1)\n",
    "        img = K.reshape(img, (-1, ) + shape[1:])\n",
    "        \n",
    "        # crop and resize image\n",
    "        # input: img(None*8, H, W, C), boxes(None*8, 4)\n",
    "        # return: tensor(None, slice_height, slice_width, C*num_boxes)\n",
    "        box_ind = K.zeros_like(boxes, 'int32')\n",
    "        box_ind = box_ind[..., 0]\n",
    "        box_ind = K.reshape(box_ind, (-1, ))\n",
    "        \n",
    "        slices = tf.image.crop_and_resize(img, boxes, box_ind, (self.pool_size, self.pool_size))\n",
    "        slices = K.reshape(slices, (-1, self.pool_size, self.pool_size, self.num_rois * shape[-1]))\n",
    "\n",
    "        if self.dim_ordering == 'th':\n",
    "            slices = K.permute_dimensions(slices, (0, 3, 1, 2))\n",
    "        else:\n",
    "            slices = K.permute_dimensions(slices, (0, 1, 2, 3))\n",
    "\n",
    "        return slices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### build model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-12T00:46:39.520232Z",
     "start_time": "2019-04-12T00:46:33.346689Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "inp_a (InputLayer)              (None, 224, 224, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv1 (Conv2D)           (None, 224, 224, 64) 1792        inp_a[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv2 (Conv2D)           (None, 224, 224, 64) 36928       block1_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block1_pool (MaxPooling2D)      (None, 112, 112, 64) 0           block1_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block2_conv1 (Conv2D)           (None, 112, 112, 128 73856       block1_pool[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block2_conv2 (Conv2D)           (None, 112, 112, 128 147584      block2_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block2_pool (MaxPooling2D)      (None, 56, 56, 128)  0           block2_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block3_conv1 (Conv2D)           (None, 56, 56, 256)  295168      block2_pool[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block3_conv2 (Conv2D)           (None, 56, 56, 256)  590080      block3_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block3_conv3 (Conv2D)           (None, 56, 56, 256)  590080      block3_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block3_pool (MaxPooling2D)      (None, 28, 28, 256)  0           block3_conv3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block4_conv1 (Conv2D)           (None, 28, 28, 512)  1180160     block3_pool[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4_conv2 (Conv2D)           (None, 28, 28, 512)  2359808     block4_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block4_conv3 (Conv2D)           (None, 28, 28, 512)  2359808     block4_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "blue_pool1 (MaxPooling2D)       (None, 14, 14, 512)  0           block4_conv3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "blue_conv1 (Conv2D)             (None, 14, 14, 512)  2359808     blue_pool1[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "blue_conv2 (Conv2D)             (None, 14, 14, 512)  2359808     blue_conv1[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "blue_conv3 (Conv2D)             (None, 14, 14, 512)  2359808     blue_conv2[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "blue_pool (MaxPooling2D)        (None, 7, 7, 512)    0           blue_conv3[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "blue_flatten (Flatten)          (None, 25088)        0           blue_pool[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "blue_fc1 (Dense)                (None, 4096)         102764544   blue_flatten[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "blue_normal1 (BatchNormalizatio (None, 4096)         16384       blue_fc1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "blue_fc1_activation (Activation (None, 4096)         0           blue_normal1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "blue_fc2 (Dense)                (None, 4096)         16781312    blue_fc1_activation[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "red_pool1 (MaxPooling2D)        (None, 14, 14, 512)  0           block4_conv3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "blue_normal2 (BatchNormalizatio (None, 4096)         16384       blue_fc2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "red_conv1 (Conv2D)              (None, 14, 14, 512)  2359808     red_pool1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "blue_fc2_activation (Activation (None, 4096)         0           blue_normal2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "red_conv2 (Conv2D)              (None, 14, 14, 512)  2359808     red_conv1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "blue_lands (Dense)              (None, 40)           163880      blue_fc2_activation[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "red_conv3 (Conv2D)              (None, 14, 14, 512)  2359808     red_conv2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "red_pool (MaxPooling2D)         (None, 7, 7, 512)    0           red_conv3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "green_roi (RoiPooling)          (None, 2, 2, 4096)   0           block4_conv3[0][0]               \n",
      "                                                                 blue_lands[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "red_flatten (Flatten)           (None, 25088)        0           red_pool[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "green_flatten (Flatten)         (None, 16384)        0           green_roi[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "red_fc1 (Dense)                 (None, 4096)         102764544   red_flatten[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "green_fc1 (Dense)               (None, 1024)         16778240    green_flatten[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concate (Concatenate)           (None, 5120)         0           red_fc1[0][0]                    \n",
      "                                                                 green_fc1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concate_normal (BatchNormalizat (None, 5120)         20480       concate[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "concate_activation (Activation) (None, 5120)         0           concate_normal[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "blue_cates (Dense)              (None, 23)           94231       blue_fc2_activation[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "concate_category (Dense)        (None, 23)           117783      concate_activation[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "attribute (Dense)               (None, 463)          2371023     concate_activation[0][0]         \n",
      "==================================================================================================\n",
      "Total params: 263,682,917\n",
      "Trainable params: 256,021,029\n",
      "Non-trainable params: 7,661,888\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def build_model():\n",
    "    \"\"\"build the Fashion Net model\n",
    "    \n",
    "    The reference paper of focal loss is DeepFashion: Powering Robust Clothes Recognition and \n",
    "    Retrieval with Rich Annotations.\n",
    "    \n",
    "    Using batchnormalization to avoid that the output of the nn is 0.\n",
    "    \n",
    "    Arguments\n",
    "        lr: the learning rate\n",
    "    \n",
    "    Return\n",
    "        model_stage1: the first stage model for train\n",
    "        model_stage2: the second stage model for train\n",
    "        model_blue: the model for the prediction of categroy, landmarks visibility and landmarks location\n",
    "    \"\"\"\n",
    "    kwargs = {'kernel_initializer': 'he_normal', 'padding': 'same'}\n",
    "    fkwargs = {'kernel_initializer': 'he_normal'}\n",
    "    \n",
    "    # base model\n",
    "    inp_a = layers.Input(shape=image_shape, name='inp_a')\n",
    "    inp_p = layers.Input(shape=image_shape, name='inp_p')\n",
    "    inp_n = layers.Input(shape=image_shape, name='inp_n')\n",
    "    \n",
    "    model = VGG16(input_tensor=inp_a, weights='imagenet')\n",
    "    # if using model.trainable = False, the block5 will be untrainable too\n",
    "    for layer in model.layers:\n",
    "        if layer.name == 'block4_pool':\n",
    "            break\n",
    "        else:\n",
    "            layer.trainable = False\n",
    "    node = model.get_layer(name='block4_conv3').output\n",
    "    \n",
    "    # blue\n",
    "    blue = layers.MaxPool2D((2, 2), strides=(2, 2), name='blue_pool1')(node)\n",
    "    blue = layers.Conv2D(512, (3, 3), activation='relu', name='blue_conv1', **kwargs)(blue)\n",
    "    blue = layers.Conv2D(512, (3, 3), activation='relu', name='blue_conv2', **kwargs)(blue)\n",
    "    blue = layers.Conv2D(512, (3, 3), activation='relu', name='blue_conv3', **kwargs)(blue)\n",
    "    blue = layers.MaxPool2D((2, 2), strides=(2, 2), name='blue_pool')(blue)   \n",
    "    \n",
    "    blue = layers.Flatten(name='blue_flatten')(blue)\n",
    "    \n",
    "    blue = layers.Dense(4096, name='blue_fc1', **fkwargs)(blue)\n",
    "    blue = layers.BatchNormalization(name='blue_normal1')(blue)\n",
    "    blue = layers.Activation('relu', name='blue_fc1_activation')(blue)\n",
    "    blue = layers.Dense(4096, name='blue_fc2', **fkwargs)(blue)\n",
    "    blue = layers.BatchNormalization(name='blue_normal2')(blue)\n",
    "    blue = layers.Activation('relu', name='blue_fc2_activation')(blue)\n",
    "    ## land marks and category loss\n",
    "    blue_cates = layers.Dense(\n",
    "        num_category,\n",
    "        kernel_initializer='he_normal',\n",
    "        activation='softmax',\n",
    "        name='blue_cates'\n",
    "    )(blue)\n",
    "    blue_lands = layers.Dense(8 * (3 + 2), kernel_initializer='he_normal', name='blue_lands')(blue)\n",
    "    model_blue = Model(inp_a, [blue_cates, blue_lands], name='model_blue')\n",
    "    \n",
    "    # red\n",
    "    red = layers.MaxPool2D((2, 2), strides=(2, 2), name='red_pool1')(node)\n",
    "    red = layers.Conv2D(512, (3, 3), activation='relu', name='red_conv1', **kwargs)(red)\n",
    "    red = layers.Conv2D(512, (3, 3), activation='relu', name='red_conv2', **kwargs)(red)\n",
    "    red = layers.Conv2D(512, (3, 3), activation='relu', name='red_conv3', **kwargs)(red)\n",
    "    red = layers.MaxPooling2D((2, 2), strides=(2, 2), name='red_pool')(red)\n",
    "    red = layers.Flatten(name='red_flatten')(red)\n",
    "    red = layers.Dense(4096, name='red_fc1', **fkwargs)(red)\n",
    "    \n",
    "    # green\n",
    "    green = RoiPooling(pool_size=2, num_rois=8, window_size=4, name='green_roi')([node, blue_lands])\n",
    "    green = layers.Flatten(name='green_flatten')(green)\n",
    "    green = layers.Dense(1024, name='green_fc1', **fkwargs)(green)\n",
    "    \n",
    "    # concate the red and green\n",
    "    red_green = layers.concatenate([red, green], name='concate')\n",
    "    red_green = layers.BatchNormalization(name='concate_normal')(red_green)\n",
    "    red_green = layers.Activation('relu', name='concate_activation')(red_green)\n",
    "    ## concate category loss\n",
    "    red_green_cates = layers.Dense(\n",
    "        num_category,\n",
    "        kernel_initializer='he_normal',\n",
    "        activation='softmax',\n",
    "        name='concate_category'\n",
    "    )(red_green)\n",
    "    ## attribute loss\n",
    "    red_green_attrs = layers.Dense(\n",
    "        num_attr,\n",
    "        kernel_initializer='he_normal',\n",
    "        activation='sigmoid',\n",
    "        name='attribute'\n",
    "    )(red_green)\n",
    "      \n",
    "    model_source = Model(inp_a, [blue_cates, blue_lands, red_green_cates, red_green_attrs])\n",
    "    \n",
    "    # turning weights\n",
    "    for layer in model.layers:\n",
    "        name = layer.name\n",
    "        if 'block5' in name:\n",
    "            blue_name = f\"blue_{name.split('_')[1]}\"\n",
    "            model_source.get_layer(blue_name).set_weights(layer.get_weights())\n",
    "            red_name = f\"red_{name.split('_')[1]}\"\n",
    "            model_source.get_layer(red_name).set_weights(layer.get_weights())\n",
    "\n",
    "    return model_source, model_blue\n",
    "    \n",
    "\n",
    "def change_stage(model, lr=3e-4, stage=1):\n",
    "    \"\"\"change the model's training stage\n",
    "    \n",
    "    Arguments\n",
    "        model: the model object\n",
    "        lr: learning rate\n",
    "        stage: if 1, blue model will be trainable, if 2, blue model will be untrainable\n",
    "    \"\"\"\n",
    "    opt = Adam(lr)\n",
    "    # stage1: train the blue node\n",
    "    if stage == 1:\n",
    "        for layer in model.layers:\n",
    "            if 'blue' in layer.name:\n",
    "                layer.trainable = True \n",
    "        model.compile(\n",
    "            opt,\n",
    "            loss={\n",
    "                'blue_cates': category_loss,\n",
    "                'blue_lands': lands_loss,\n",
    "                'concate_category': category_loss,\n",
    "                'attribute': attribute_loss,\n",
    "            },\n",
    "            loss_weights={\n",
    "                'blue_cates': 1.,\n",
    "                'blue_lands': 1.,\n",
    "                'concate_category': 0.1,\n",
    "                'attribute': 0.1,\n",
    "            },\n",
    "            metrics={\n",
    "                'blue_cates': [top1, top2, top3, top5]\n",
    "            }\n",
    "        )\n",
    "    # stage2: train the red and green node\n",
    "    elif stage == 2:\n",
    "        for layer in model.layers:\n",
    "            if 'blue' in layer.name:\n",
    "                layer.trainable = False\n",
    "                \n",
    "        model.compile(\n",
    "            opt,\n",
    "            loss={\n",
    "                'blue_cates': category_loss,\n",
    "                'blue_lands': lands_loss,\n",
    "                'concate_category': category_loss,\n",
    "                'attribute': attribute_loss\n",
    "            },\n",
    "            loss_weights={\n",
    "                'blue_cates': 0.,\n",
    "                'blue_lands': 0.,\n",
    "                'concate_category': 1.,\n",
    "                'attribute': 1.\n",
    "            },\n",
    "            metrics={\n",
    "                'concate_category': [top1, top2, top3, top5]\n",
    "            }\n",
    "        )\n",
    "    return model\n",
    "    \n",
    "model_source, model_blue = build_model()\n",
    "model = change_stage(model_source); model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-12T00:46:39.544850Z",
     "start_time": "2019-04-12T00:46:39.523261Z"
    }
   },
   "outputs": [],
   "source": [
    "class LRFinder(object):\n",
    "    \"\"\"Plots the change of the loss function of a Keras model when the learning rate is exponentially\n",
    "    increasing. See for details: \n",
    "    https://towardsdatascience.com/estimating-optimal-learning-rate-for-a-deep-neural-network-ce32f2556ce0\n",
    "    \"\"\"\n",
    "    def __init__(self, model):\n",
    "        self.model = model\n",
    "        self.losses = []\n",
    "        self.lrs = []\n",
    "        self.best_loss = 1e9\n",
    "\n",
    "    def on_batch_end(self, batch, logs):\n",
    "        # Log the learning rate\n",
    "        lr = K.get_value(self.model.optimizer.lr)\n",
    "        self.lrs.append(lr)\n",
    "\n",
    "        # Log the loss\n",
    "        loss = logs['loss']\n",
    "        self.losses.append(loss)\n",
    "\n",
    "        # Check whether the loss got too large or NaN\n",
    "        if math.isnan(loss) or loss > self.best_loss * 4:\n",
    "            self.model.stop_training = True\n",
    "            return\n",
    "\n",
    "        if loss < self.best_loss:\n",
    "            self.best_loss = loss\n",
    "\n",
    "        # Increase the learning rate for the next batch\n",
    "        lr *= self.lr_mult\n",
    "        K.set_value(self.model.optimizer.lr, lr)\n",
    "\n",
    "    def find(self, data_gen, start_lr, end_lr, epochs=1, class_weight=None):\n",
    "        num_batches = epochs * len(data_gen)\n",
    "        self.lr_mult = (float(end_lr) / float(start_lr)) ** (float(1) / float(num_batches))\n",
    "\n",
    "        # Save weights into a file\n",
    "        self.model.save_weights('tmp.h5')\n",
    "\n",
    "        # Remember the original learning rate\n",
    "        original_lr = K.get_value(self.model.optimizer.lr)\n",
    "\n",
    "        # Set the initial learning rate\n",
    "        K.set_value(self.model.optimizer.lr, start_lr)\n",
    "\n",
    "        callback = LambdaCallback(on_batch_end=lambda batch, logs: self.on_batch_end(batch, logs))\n",
    "\n",
    "        self.model.fit_generator(data_gen, epochs=epochs, callbacks=[callback], class_weight=class_weight)\n",
    "\n",
    "        # Restore the weights to the state before model fitting\n",
    "        self.model.load_weights('tmp.h5')\n",
    "\n",
    "        # Restore the original learning rate\n",
    "        K.set_value(self.model.optimizer.lr, original_lr)\n",
    "\n",
    "    def plot_loss(self, n_skip_beginning=10, n_skip_end=5):\n",
    "        \"\"\"\n",
    "        Plots the loss.\n",
    "        Parameters:\n",
    "            n_skip_beginning - number of batches to skip on the left.\n",
    "            n_skip_end - number of batches to skip on the right.\n",
    "        \"\"\"\n",
    "        plt.ylabel(\"loss\")\n",
    "        plt.xlabel(\"learning rate (log scale)\")\n",
    "        plt.plot(self.lrs[n_skip_beginning:-n_skip_end], self.losses[n_skip_beginning:-n_skip_end])\n",
    "        plt.xscale('log')\n",
    "\n",
    "    def plot_loss_change(self, sma=1, n_skip_beginning=10, n_skip_end=5, y_lim=(-0.01, 0.01)):\n",
    "        \"\"\"\n",
    "        Plots rate of change of the loss function.\n",
    "        Parameters:\n",
    "            sma - number of batches for simple moving average to smooth out the curve.\n",
    "            n_skip_beginning - number of batches to skip on the left.\n",
    "            n_skip_end - number of batches to skip on the right.\n",
    "            y_lim - limits for the y axis.\n",
    "        \"\"\"\n",
    "        assert sma >= 1\n",
    "        derivatives = [0] * sma\n",
    "        for i in range(sma, len(self.lrs)):\n",
    "            derivative = (self.losses[i] - self.losses[i - sma]) / sma\n",
    "            derivatives.append(derivative)\n",
    "\n",
    "        plt.ylabel(\"rate of loss change\")\n",
    "        plt.xlabel(\"learning rate (log scale)\")\n",
    "        plt.plot(self.lrs[n_skip_beginning:-n_skip_end], derivatives[n_skip_beginning:-n_skip_end])\n",
    "        plt.xscale('log')\n",
    "        plt.ylim(y_lim)\n",
    "    \n",
    "    def plot_loss_smoothing(self, smoothing=0.6, n_skip_beginning=10, n_skip_end=5):\n",
    "        \"\"\"plot smoothing as like tensorboard\n",
    "        \"\"\"\n",
    "        losses, lrs = self.losses[n_skip_beginning:-n_skip_end], self.lrs[n_skip_beginning:-n_skip_end]\n",
    "        last = losses[0]\n",
    "        smoothes = []\n",
    "        for point in losses:\n",
    "            smooth = last * smoothing + (1-smoothing) * point\n",
    "            smoothes.append(smooth)\n",
    "            last = point\n",
    "        \n",
    "        plt.ylabel(\"rate of loss smoothing\")\n",
    "        plt.xlabel(\"learning rate (log scale)\")\n",
    "        plt.plot(lrs, smoothes)\n",
    "        plt.xscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-12T00:46:39.570926Z",
     "start_time": "2019-04-12T00:46:39.548754Z"
    }
   },
   "outputs": [],
   "source": [
    "def set_lr(model, lr):\n",
    "    \"\"\"config the model's learning rate\"\"\"\n",
    "    K.set_value(model.optimizer.lr, float(lr))\n",
    "    \n",
    "    \n",
    "def get_lr(model):\n",
    "    \"\"\"get the model's learning rate\"\"\"\n",
    "    return K.get_value(model.optimizer.lr)\n",
    "\n",
    "\n",
    "def save_model(model):\n",
    "    \"\"\"save the model of current step into the MODEL_PATH\"\"\"\n",
    "    model.save(f'{MODEL_PATH}epoch{steps}.h5')\n",
    "    \n",
    "    \n",
    "def load_model(model, temp_step):\n",
    "    \"\"\"load model from what the function save_model saved\n",
    "    \n",
    "    Arguments\n",
    "        model: the model have been built\n",
    "        temp_step: which step of the model you want to load\n",
    "    \n",
    "    Return\n",
    "        the loaded model\n",
    "    \"\"\"\n",
    "    global steps\n",
    "    steps = temp_step\n",
    "    \n",
    "    model_file = f'{MODEL_PATH}epoch{steps}.h5'\n",
    "    tmp = keras.models.load_model(model_file, compile=False)\n",
    "    model.set_weights(tmp.get_weights())\n",
    "    return model\n",
    "    \n",
    "\n",
    "def load_modelfile(model, modelfile):\n",
    "    \"\"\"load the model from model path\n",
    "    \n",
    "    Arguments\n",
    "        model: the model have been built\n",
    "        modelfile: the model file path, such as '../data/output/models/fashionnet/epoch.04-0.22-0.333.h5' or\n",
    "            'epoch.04-0.22-0.333.h5'\n",
    "    \n",
    "    Return\n",
    "        the loaded model\n",
    "    \"\"\"\n",
    "    global steps\n",
    "    steps = int(modelfile.split('-')[0].split('.')[-1])\n",
    "    \n",
    "    model_file = f'{MODEL_PATH}{modelfile}'\n",
    "#     tmp = keras.models.load_model(model_file, compile=False, custom_objects={\n",
    "#         'RoiPooling': RoiPooling\n",
    "#     })\n",
    "#     model.set_weights(tmp.get_weights())\n",
    "    model.load_weights(model_file, by_name=True)\n",
    "    return model\n",
    "    \n",
    "    \n",
    "def callbacks():\n",
    "    \"\"\"get the configured keras train callbacks\"\"\"\n",
    "    ckpt_file = MODEL_PATH + 'ckpt_model.{epoch:02d}-{val_loss:.4f}-{val_blue_cates_loss:.4f}.h5'\n",
    "    checkpoint = ModelCheckpoint(ckpt_file, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n",
    "    \n",
    "    csv_log_file = f'{LOGGER_PATH}{model_dict[no_model]}.csv'\n",
    "    csv_log = CSVLogger(csv_log_file)\n",
    "    \n",
    "    tensorboard = TensorBoard(f'{LOGGER_PATH}')\n",
    "    \n",
    "    return [checkpoint, csv_log, tensorboard]\n",
    "\n",
    "\n",
    "def make_steps(step):\n",
    "    \"\"\"training some epoches\n",
    "    \n",
    "    Arguments\n",
    "        step: how many epoches the model will be trained \n",
    "    \"\"\"\n",
    "    global steps, histories\n",
    "\n",
    "    # Train the model for 'step' epochs\n",
    "    history = model.fit_generator(\n",
    "        TrainSequence(train_df, batch_size=64),\n",
    "        validation_data=ValSequence(val_df, batch_size=64),\n",
    "        callbacks=callbacks(),\n",
    "        initial_epoch=steps, epochs=steps + step, max_queue_size=12, workers=8, verbose=1).history\n",
    "    steps += step\n",
    "\n",
    "    # Collect history data\n",
    "    history['epochs'] = steps\n",
    "    history['lr'] = get_lr(model)\n",
    "    print(history['epochs'], history['lr'])\n",
    "    histories.append(history)\n",
    "    \n",
    "    \n",
    "def find_lr(model, start_lr=3e-5, end_lr=1):\n",
    "    \"\"\"plot the loss-lr curve to find an appropriate model\"\"\"\n",
    "    lr_finder = LRFinder(model)\n",
    "    lr_finder.find(\n",
    "        TrainSequence(train_df, batch_size=8),\n",
    "        start_lr=start_lr, end_lr=end_lr\n",
    "    )\n",
    "    lr_finder.plot_loss()\n",
    "    return lr_finder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### StartTrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-12T00:46:40.693019Z",
     "start_time": "2019-04-12T00:46:40.688404Z"
    }
   },
   "outputs": [],
   "source": [
    "histories = []\n",
    "steps = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### stage1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "找到上阶段最佳模型，使用第1阶段模型继续训练。如果一开始训练，准确率就高达99%，那么多半是初始化异常，重新启动初始化就好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2019-04-11T08:28:00.870Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "412/412 [==============================] - 298s 723ms/step - loss: 8.2292 - blue_cates_loss: 1.0935 - blue_lands_loss: 5.7728 - concate_category_loss: 1.1747 - attribute_loss: 12.4544 - blue_cates_top1: 0.2601 - blue_cates_top2: 0.4334 - blue_cates_top3: 0.5584 - blue_cates_top5: 0.7161 - val_loss: 6.0250 - val_blue_cates_loss: 1.1286 - val_blue_lands_loss: 4.2548 - val_concate_category_loss: 1.3687 - val_attribute_loss: 5.0481 - val_blue_cates_top1: 0.3963 - val_blue_cates_top2: 0.6101 - val_blue_cates_top3: 0.7360 - val_blue_cates_top5: 0.8754\n",
      "\n",
      "Epoch 00001: val_loss improved from inf to 6.02505, saving model to ../data/output/models/fashionnet/ckpt_model.01-6.0250-1.1286.h5\n",
      "Epoch 2/30\n",
      "412/412 [==============================] - 286s 694ms/step - loss: 6.0445 - blue_cates_loss: 0.8297 - blue_lands_loss: 4.6452 - concate_category_loss: 0.9183 - attribute_loss: 4.7785 - blue_cates_top1: 0.3797 - blue_cates_top2: 0.5925 - blue_cates_top3: 0.7194 - blue_cates_top5: 0.8580 - val_loss: 4.8643 - val_blue_cates_loss: 0.9426 - val_blue_lands_loss: 3.3513 - val_concate_category_loss: 1.0675 - val_attribute_loss: 4.6363 - val_blue_cates_top1: 0.4241 - val_blue_cates_top2: 0.6641 - val_blue_cates_top3: 0.7901 - val_blue_cates_top5: 0.9163\n",
      "\n",
      "Epoch 00002: val_loss improved from 6.02505 to 4.86426, saving model to ../data/output/models/fashionnet/ckpt_model.02-4.8643-0.9426.h5\n",
      "Epoch 3/30\n",
      "412/412 [==============================] - 287s 697ms/step - loss: 5.4606 - blue_cates_loss: 0.6701 - blue_lands_loss: 4.2639 - concate_category_loss: 0.6607 - attribute_loss: 4.6061 - blue_cates_top1: 0.4384 - blue_cates_top2: 0.6577 - blue_cates_top3: 0.7763 - blue_cates_top5: 0.8974 - val_loss: 4.7716 - val_blue_cates_loss: 0.8839 - val_blue_lands_loss: 3.3384 - val_concate_category_loss: 0.9549 - val_attribute_loss: 4.5376 - val_blue_cates_top1: 0.4723 - val_blue_cates_top2: 0.6825 - val_blue_cates_top3: 0.8062 - val_blue_cates_top5: 0.9238\n",
      "\n",
      "Epoch 00003: val_loss improved from 4.86426 to 4.77156, saving model to ../data/output/models/fashionnet/ckpt_model.03-4.7716-0.8839.h5\n",
      "Epoch 4/30\n",
      "412/412 [==============================] - 287s 698ms/step - loss: 5.0428 - blue_cates_loss: 0.5686 - blue_lands_loss: 3.9616 - concate_category_loss: 0.6109 - attribute_loss: 4.5152 - blue_cates_top1: 0.4870 - blue_cates_top2: 0.7022 - blue_cates_top3: 0.8112 - blue_cates_top5: 0.9183 - val_loss: 4.9501 - val_blue_cates_loss: 1.0547 - val_blue_lands_loss: 3.3314 - val_concate_category_loss: 1.0548 - val_attribute_loss: 4.5849 - val_blue_cates_top1: 0.4907 - val_blue_cates_top2: 0.6999 - val_blue_cates_top3: 0.8004 - val_blue_cates_top5: 0.9094\n",
      "\n",
      "Epoch 00004: val_loss did not improve from 4.77156\n",
      "Epoch 5/30\n",
      "412/412 [==============================] - 289s 702ms/step - loss: 4.9529 - blue_cates_loss: 0.5953 - blue_lands_loss: 3.8503 - concate_category_loss: 0.6026 - attribute_loss: 4.4701 - blue_cates_top1: 0.4960 - blue_cates_top2: 0.7159 - blue_cates_top3: 0.8262 - blue_cates_top5: 0.9287 - val_loss: 4.5564 - val_blue_cates_loss: 0.8409 - val_blue_lands_loss: 3.1721 - val_concate_category_loss: 0.9587 - val_attribute_loss: 4.4745 - val_blue_cates_top1: 0.5545 - val_blue_cates_top2: 0.7610 - val_blue_cates_top3: 0.8622 - val_blue_cates_top5: 0.9488\n",
      "\n",
      "Epoch 00005: val_loss improved from 4.77156 to 4.55639, saving model to ../data/output/models/fashionnet/ckpt_model.05-4.5564-0.8409.h5\n",
      "Epoch 6/30\n",
      "412/412 [==============================] - 296s 719ms/step - loss: 4.6848 - blue_cates_loss: 0.5230 - blue_lands_loss: 3.6680 - concate_category_loss: 0.5302 - attribute_loss: 4.4080 - blue_cates_top1: 0.5341 - blue_cates_top2: 0.7443 - blue_cates_top3: 0.8479 - blue_cates_top5: 0.9407 - val_loss: 4.5950 - val_blue_cates_loss: 1.0240 - val_blue_lands_loss: 2.9954 - val_concate_category_loss: 1.2328 - val_attribute_loss: 4.5233 - val_blue_cates_top1: 0.5625 - val_blue_cates_top2: 0.7585 - val_blue_cates_top3: 0.8649 - val_blue_cates_top5: 0.9528\n",
      "\n",
      "Epoch 00006: val_loss did not improve from 4.55639\n",
      "Epoch 7/30\n",
      "412/412 [==============================] - 291s 706ms/step - loss: 4.4290 - blue_cates_loss: 0.4559 - blue_lands_loss: 3.4915 - concate_category_loss: 0.4738 - attribute_loss: 4.3430 - blue_cates_top1: 0.5571 - blue_cates_top2: 0.7653 - blue_cates_top3: 0.8658 - blue_cates_top5: 0.9486 - val_loss: 4.4441 - val_blue_cates_loss: 0.8485 - val_blue_lands_loss: 3.0629 - val_concate_category_loss: 0.8648 - val_attribute_loss: 4.4610 - val_blue_cates_top1: 0.6077 - val_blue_cates_top2: 0.7986 - val_blue_cates_top3: 0.8839 - val_blue_cates_top5: 0.9594\n",
      "\n",
      "Epoch 00007: val_loss improved from 4.55639 to 4.44408, saving model to ../data/output/models/fashionnet/ckpt_model.07-4.4441-0.8485.h5\n",
      "Epoch 8/30\n",
      "412/412 [==============================] - 290s 703ms/step - loss: 4.3509 - blue_cates_loss: 0.4497 - blue_lands_loss: 3.4301 - concate_category_loss: 0.4306 - attribute_loss: 4.2806 - blue_cates_top1: 0.5710 - blue_cates_top2: 0.7796 - blue_cates_top3: 0.8732 - blue_cates_top5: 0.9546 - val_loss: 5.2741 - val_blue_cates_loss: 1.1724 - val_blue_lands_loss: 3.5320 - val_concate_category_loss: 1.1469 - val_attribute_loss: 4.5500 - val_blue_cates_top1: 0.5996 - val_blue_cates_top2: 0.7903 - val_blue_cates_top3: 0.8711 - val_blue_cates_top5: 0.9480\n",
      "\n",
      "Epoch 00008: val_loss did not improve from 4.44408\n",
      "Epoch 9/30\n",
      "412/412 [==============================] - 292s 709ms/step - loss: 4.2218 - blue_cates_loss: 0.4335 - blue_lands_loss: 3.3302 - concate_category_loss: 0.3714 - attribute_loss: 4.2097 - blue_cates_top1: 0.5844 - blue_cates_top2: 0.7906 - blue_cates_top3: 0.8788 - blue_cates_top5: 0.9560 - val_loss: 4.2889 - val_blue_cates_loss: 0.8485 - val_blue_lands_loss: 2.8947 - val_concate_category_loss: 1.0261 - val_attribute_loss: 4.4309 - val_blue_cates_top1: 0.5750 - val_blue_cates_top2: 0.7774 - val_blue_cates_top3: 0.8758 - val_blue_cates_top5: 0.9556\n",
      "\n",
      "Epoch 00009: val_loss improved from 4.44408 to 4.28894, saving model to ../data/output/models/fashionnet/ckpt_model.09-4.2889-0.8485.h5\n",
      "Epoch 10/30\n",
      "412/412 [==============================] - 291s 705ms/step - loss: 4.0830 - blue_cates_loss: 0.4076 - blue_lands_loss: 3.2228 - concate_category_loss: 0.3635 - attribute_loss: 4.1627 - blue_cates_top1: 0.5973 - blue_cates_top2: 0.8020 - blue_cates_top3: 0.8920 - blue_cates_top5: 0.9619 - val_loss: 4.2536 - val_blue_cates_loss: 0.8696 - val_blue_lands_loss: 2.8371 - val_concate_category_loss: 1.0133 - val_attribute_loss: 4.4555 - val_blue_cates_top1: 0.5757 - val_blue_cates_top2: 0.7767 - val_blue_cates_top3: 0.8684 - val_blue_cates_top5: 0.9527\n",
      "\n",
      "Epoch 00010: val_loss improved from 4.28894 to 4.25361, saving model to ../data/output/models/fashionnet/ckpt_model.10-4.2536-0.8696.h5\n",
      "Epoch 11/30\n",
      "412/412 [==============================] - 290s 705ms/step - loss: 3.9586 - blue_cates_loss: 0.3835 - blue_lands_loss: 3.1320 - concate_category_loss: 0.3257 - attribute_loss: 4.1050 - blue_cates_top1: 0.6098 - blue_cates_top2: 0.8073 - blue_cates_top3: 0.8921 - blue_cates_top5: 0.9625 - val_loss: 4.7794 - val_blue_cates_loss: 1.0734 - val_blue_lands_loss: 3.0968 - val_concate_category_loss: 1.1468 - val_attribute_loss: 4.9450 - val_blue_cates_top1: 0.5268 - val_blue_cates_top2: 0.7499 - val_blue_cates_top3: 0.8587 - val_blue_cates_top5: 0.9482\n",
      "\n",
      "Epoch 00011: val_loss did not improve from 4.25361\n",
      "Epoch 12/30\n",
      "412/412 [==============================] - 293s 712ms/step - loss: 3.9888 - blue_cates_loss: 0.4226 - blue_lands_loss: 3.1292 - concate_category_loss: 0.3111 - attribute_loss: 4.0583 - blue_cates_top1: 0.6040 - blue_cates_top2: 0.8086 - blue_cates_top3: 0.8947 - blue_cates_top5: 0.9647 - val_loss: 4.3007 - val_blue_cates_loss: 0.9189 - val_blue_lands_loss: 2.8495 - val_concate_category_loss: 0.8697 - val_attribute_loss: 4.4534 - val_blue_cates_top1: 0.6186 - val_blue_cates_top2: 0.8128 - val_blue_cates_top3: 0.8990 - val_blue_cates_top5: 0.9638\n",
      "\n",
      "Epoch 00012: val_loss did not improve from 4.25361\n",
      "Epoch 13/30\n",
      "412/412 [==============================] - 296s 719ms/step - loss: 3.7529 - blue_cates_loss: 0.3582 - blue_lands_loss: 2.9681 - concate_category_loss: 0.2804 - attribute_loss: 3.9855 - blue_cates_top1: 0.6342 - blue_cates_top2: 0.8247 - blue_cates_top3: 0.9077 - blue_cates_top5: 0.9713 - val_loss: 4.3281 - val_blue_cates_loss: 0.7806 - val_blue_lands_loss: 2.9985 - val_concate_category_loss: 0.9859 - val_attribute_loss: 4.5040 - val_blue_cates_top1: 0.6186 - val_blue_cates_top2: 0.8067 - val_blue_cates_top3: 0.8918 - val_blue_cates_top5: 0.9627\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 4.25361\n",
      "Epoch 14/30\n",
      "412/412 [==============================] - 284s 689ms/step - loss: 3.6534 - blue_cates_loss: 0.3367 - blue_lands_loss: 2.9011 - concate_category_loss: 0.2405 - attribute_loss: 3.9146 - blue_cates_top1: 0.6490 - blue_cates_top2: 0.8378 - blue_cates_top3: 0.9165 - blue_cates_top5: 0.9751 - val_loss: 4.4683 - val_blue_cates_loss: 1.0197 - val_blue_lands_loss: 2.8805 - val_concate_category_loss: 1.1129 - val_attribute_loss: 4.5682 - val_blue_cates_top1: 0.5999 - val_blue_cates_top2: 0.7916 - val_blue_cates_top3: 0.8795 - val_blue_cates_top5: 0.9581\n",
      "\n",
      "Epoch 00014: val_loss did not improve from 4.25361\n",
      "Epoch 15/30\n",
      "412/412 [==============================] - 285s 692ms/step - loss: 3.6593 - blue_cates_loss: 0.3707 - blue_lands_loss: 2.8747 - concate_category_loss: 0.2574 - attribute_loss: 3.8816 - blue_cates_top1: 0.6431 - blue_cates_top2: 0.8370 - blue_cates_top3: 0.9140 - blue_cates_top5: 0.9730 - val_loss: 4.5000 - val_blue_cates_loss: 1.0235 - val_blue_lands_loss: 2.9237 - val_concate_category_loss: 0.9307 - val_attribute_loss: 4.5973 - val_blue_cates_top1: 0.6046 - val_blue_cates_top2: 0.8023 - val_blue_cates_top3: 0.8945 - val_blue_cates_top5: 0.9648\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 4.25361\n",
      "Epoch 16/30\n",
      "412/412 [==============================] - 284s 689ms/step - loss: 3.4929 - blue_cates_loss: 0.3120 - blue_lands_loss: 2.7800 - concate_category_loss: 0.2134 - attribute_loss: 3.7956 - blue_cates_top1: 0.6664 - blue_cates_top2: 0.8509 - blue_cates_top3: 0.9258 - blue_cates_top5: 0.9784 - val_loss: 4.5036 - val_blue_cates_loss: 0.9150 - val_blue_lands_loss: 3.0111 - val_concate_category_loss: 1.0723 - val_attribute_loss: 4.7028 - val_blue_cates_top1: 0.6018 - val_blue_cates_top2: 0.7905 - val_blue_cates_top3: 0.8846 - val_blue_cates_top5: 0.9562\n",
      "\n",
      "Epoch 00016: val_loss did not improve from 4.25361\n",
      "Epoch 17/30\n",
      "  2/412 [..............................] - ETA: 2:51 - loss: 3.3596 - blue_cates_loss: 0.2572 - blue_lands_loss: 2.7113 - concate_category_loss: 0.1914 - attribute_loss: 3.7204 - blue_cates_top1: 0.7188 - blue_cates_top2: 0.8750 - blue_cates_top3: 0.9297 - blue_cates_top5: 0.9766"
     ]
    }
   ],
   "source": [
    "model = change_stage(model, lr=1e-4, stage=1)\n",
    "make_steps(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### stage2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "找到上阶段最佳模型，使用第2阶段模型继续训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-11T10:06:54.971042Z",
     "start_time": "2019-04-11T10:06:53.525348Z"
    }
   },
   "outputs": [],
   "source": [
    "model = load_modelfile(model, 'ckpt_model.10-4.2536-0.8696.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-11T10:59:51.202914Z",
     "start_time": "2019-04-11T10:07:36.391014Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11/40\n",
      "412/412 [==============================] - 286s 694ms/step - loss: 4.4701 - blue_cates_loss: 0.3534 - blue_lands_loss: 3.0721 - concate_category_loss: 0.3432 - attribute_loss: 4.1269 - concate_category_top1: 0.6622 - concate_category_top2: 0.8460 - concate_category_top3: 0.9208 - concate_category_top5: 0.9766 - val_loss: 5.5414 - val_blue_cates_loss: 0.8696 - val_blue_lands_loss: 2.8371 - val_concate_category_loss: 1.0636 - val_attribute_loss: 4.4778 - val_concate_category_top1: 0.6311 - val_concate_category_top2: 0.8193 - val_concate_category_top3: 0.8974 - val_concate_category_top5: 0.9630\n",
      "\n",
      "Epoch 00011: val_loss improved from inf to 5.54141, saving model to ../data/output/models/fashionnet/ckpt_model.11-5.5414-0.8696.h5\n",
      "Epoch 12/40\n",
      "412/412 [==============================] - 284s 688ms/step - loss: 4.4421 - blue_cates_loss: 0.3630 - blue_lands_loss: 3.0824 - concate_category_loss: 0.3527 - attribute_loss: 4.0894 - concate_category_top1: 0.6673 - concate_category_top2: 0.8483 - concate_category_top3: 0.9231 - concate_category_top5: 0.9765 - val_loss: 5.3285 - val_blue_cates_loss: 0.8696 - val_blue_lands_loss: 2.8371 - val_concate_category_loss: 0.8171 - val_attribute_loss: 4.5114 - val_concate_category_top1: 0.5808 - val_concate_category_top2: 0.7797 - val_concate_category_top3: 0.8728 - val_concate_category_top5: 0.9494\n",
      "\n",
      "Epoch 00012: val_loss improved from 5.54141 to 5.32847, saving model to ../data/output/models/fashionnet/ckpt_model.12-5.3285-0.8696.h5\n",
      "Epoch 13/40\n",
      "412/412 [==============================] - 286s 695ms/step - loss: 4.2498 - blue_cates_loss: 0.3483 - blue_lands_loss: 3.0738 - concate_category_loss: 0.2692 - attribute_loss: 3.9806 - concate_category_top1: 0.7042 - concate_category_top2: 0.8761 - concate_category_top3: 0.9387 - concate_category_top5: 0.9832 - val_loss: 5.6629 - val_blue_cates_loss: 0.8696 - val_blue_lands_loss: 2.8371 - val_concate_category_loss: 1.1745 - val_attribute_loss: 4.4884 - val_concate_category_top1: 0.6419 - val_concate_category_top2: 0.8254 - val_concate_category_top3: 0.9026 - val_concate_category_top5: 0.9645\n",
      "\n",
      "Epoch 00013: val_loss did not improve from 5.32847\n",
      "Epoch 14/40\n",
      "412/412 [==============================] - 283s 687ms/step - loss: 4.1581 - blue_cates_loss: 0.3517 - blue_lands_loss: 3.0680 - concate_category_loss: 0.2485 - attribute_loss: 3.9096 - concate_category_top1: 0.7261 - concate_category_top2: 0.8881 - concate_category_top3: 0.9480 - concate_category_top5: 0.9865 - val_loss: 5.6570 - val_blue_cates_loss: 0.8696 - val_blue_lands_loss: 2.8371 - val_concate_category_loss: 1.1030 - val_attribute_loss: 4.5539 - val_concate_category_top1: 0.6428 - val_concate_category_top2: 0.8290 - val_concate_category_top3: 0.9037 - val_concate_category_top5: 0.9631\n",
      "\n",
      "Epoch 00014: val_loss did not improve from 5.32847\n",
      "Epoch 15/40\n",
      "412/412 [==============================] - 283s 688ms/step - loss: 4.1012 - blue_cates_loss: 0.3581 - blue_lands_loss: 3.0594 - concate_category_loss: 0.2446 - attribute_loss: 3.8566 - concate_category_top1: 0.7366 - concate_category_top2: 0.8931 - concate_category_top3: 0.9495 - concate_category_top5: 0.9867 - val_loss: 5.6166 - val_blue_cates_loss: 0.8696 - val_blue_lands_loss: 2.8371 - val_concate_category_loss: 1.0450 - val_attribute_loss: 4.5716 - val_concate_category_top1: 0.6511 - val_concate_category_top2: 0.8332 - val_concate_category_top3: 0.9071 - val_concate_category_top5: 0.9636\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 5.32847\n",
      "Epoch 16/40\n",
      "412/412 [==============================] - 286s 694ms/step - loss: 4.0925 - blue_cates_loss: 0.3708 - blue_lands_loss: 3.0678 - concate_category_loss: 0.2644 - attribute_loss: 3.8282 - concate_category_top1: 0.7317 - concate_category_top2: 0.8934 - concate_category_top3: 0.9495 - concate_category_top5: 0.9862 - val_loss: 6.0418 - val_blue_cates_loss: 0.8696 - val_blue_lands_loss: 2.8371 - val_concate_category_loss: 1.2852 - val_attribute_loss: 4.7567 - val_concate_category_top1: 0.6440 - val_concate_category_top2: 0.8316 - val_concate_category_top3: 0.9040 - val_concate_category_top5: 0.9597\n",
      "\n",
      "Epoch 00016: val_loss did not improve from 5.32847\n",
      "Epoch 17/40\n",
      "412/412 [==============================] - 279s 678ms/step - loss: 3.9698 - blue_cates_loss: 0.3595 - blue_lands_loss: 3.0615 - concate_category_loss: 0.2220 - attribute_loss: 3.7478 - concate_category_top1: 0.7517 - concate_category_top2: 0.9024 - concate_category_top3: 0.9547 - concate_category_top5: 0.9889 - val_loss: 5.8604 - val_blue_cates_loss: 0.8696 - val_blue_lands_loss: 2.8371 - val_concate_category_loss: 1.1199 - val_attribute_loss: 4.7405 - val_concate_category_top1: 0.6661 - val_concate_category_top2: 0.8394 - val_concate_category_top3: 0.9104 - val_concate_category_top5: 0.9663\n",
      "\n",
      "Epoch 00017: val_loss did not improve from 5.32847\n",
      "Epoch 18/40\n",
      "412/412 [==============================] - 279s 676ms/step - loss: 3.8860 - blue_cates_loss: 0.3580 - blue_lands_loss: 3.0890 - concate_category_loss: 0.2032 - attribute_loss: 3.6828 - concate_category_top1: 0.7698 - concate_category_top2: 0.9146 - concate_category_top3: 0.9612 - concate_category_top5: 0.9911 - val_loss: 6.0092 - val_blue_cates_loss: 0.8696 - val_blue_lands_loss: 2.8371 - val_concate_category_loss: 1.2979 - val_attribute_loss: 4.7113 - val_concate_category_top1: 0.6476 - val_concate_category_top2: 0.8300 - val_concate_category_top3: 0.9052 - val_concate_category_top5: 0.9610\n",
      "\n",
      "Epoch 00018: val_loss did not improve from 5.32847\n",
      "Epoch 19/40\n",
      "412/412 [==============================] - 287s 696ms/step - loss: 3.7830 - blue_cates_loss: 0.3595 - blue_lands_loss: 3.0779 - concate_category_loss: 0.1756 - attribute_loss: 3.6074 - concate_category_top1: 0.7859 - concate_category_top2: 0.9254 - concate_category_top3: 0.9676 - concate_category_top5: 0.9926 - val_loss: 5.9281 - val_blue_cates_loss: 0.8696 - val_blue_lands_loss: 2.8371 - val_concate_category_loss: 1.1902 - val_attribute_loss: 4.7380 - val_concate_category_top1: 0.6461 - val_concate_category_top2: 0.8286 - val_concate_category_top3: 0.9019 - val_concate_category_top5: 0.9631\n",
      "\n",
      "Epoch 00019: val_loss did not improve from 5.32847\n",
      "Epoch 20/40\n",
      "412/412 [==============================] - 279s 677ms/step - loss: 3.6975 - blue_cates_loss: 0.3526 - blue_lands_loss: 3.0662 - concate_category_loss: 0.1604 - attribute_loss: 3.5371 - concate_category_top1: 0.8058 - concate_category_top2: 0.9337 - concate_category_top3: 0.9720 - concate_category_top5: 0.9944 - val_loss: 6.2617 - val_blue_cates_loss: 0.8696 - val_blue_lands_loss: 2.8371 - val_concate_category_loss: 1.3070 - val_attribute_loss: 4.9547 - val_concate_category_top1: 0.6688 - val_concate_category_top2: 0.8474 - val_concate_category_top3: 0.9107 - val_concate_category_top5: 0.9634\n",
      "\n",
      "Epoch 00020: val_loss did not improve from 5.32847\n",
      "Epoch 21/40\n",
      "412/412 [==============================] - 285s 691ms/step - loss: 3.6463 - blue_cates_loss: 0.3661 - blue_lands_loss: 3.0497 - concate_category_loss: 0.1601 - attribute_loss: 3.4862 - concate_category_top1: 0.8075 - concate_category_top2: 0.9337 - concate_category_top3: 0.9722 - concate_category_top5: 0.9939 - val_loss: 6.3913 - val_blue_cates_loss: 0.8696 - val_blue_lands_loss: 2.8371 - val_concate_category_loss: 1.3306 - val_attribute_loss: 5.0607 - val_concate_category_top1: 0.6641 - val_concate_category_top2: 0.8465 - val_concate_category_top3: 0.9130 - val_concate_category_top5: 0.9658\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 5.32847\n",
      "Epoch 22/40\n",
      " 10/412 [..............................] - ETA: 2:25 - loss: 3.5283 - blue_cates_loss: 0.4159 - blue_lands_loss: 3.0810 - concate_category_loss: 0.1418 - attribute_loss: 3.3865 - concate_category_top1: 0.8328 - concate_category_top2: 0.9391 - concate_category_top3: 0.9703 - concate_category_top5: 0.9969"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-e44385d408e2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mchange_stage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1e-4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmake_steps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-16-219b9c883c54>\u001b[0m in \u001b[0;36mmake_steps\u001b[0;34m(step)\u001b[0m\n\u001b[1;32m     82\u001b[0m         \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mValSequence\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m         \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m         initial_epoch=steps, epochs=steps + step, max_queue_size=12, workers=8, verbose=1).history\n\u001b[0m\u001b[1;32m     85\u001b[0m     \u001b[0msteps\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/image-search/lib/python3.6/site-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     89\u001b[0m                 warnings.warn('Update your `' + object_name + '` call to the ' +\n\u001b[1;32m     90\u001b[0m                               'Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/image-search/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m   1416\u001b[0m             \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1417\u001b[0m             \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1418\u001b[0;31m             initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1420\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0minterfaces\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegacy_generator_methods_support\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/image-search/lib/python3.6/site-packages/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m    215\u001b[0m                 outs = model.train_on_batch(x, y,\n\u001b[1;32m    216\u001b[0m                                             \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 217\u001b[0;31m                                             class_weight=class_weight)\n\u001b[0m\u001b[1;32m    218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/image-search/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[1;32m   1215\u001b[0m             \u001b[0mins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1216\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1217\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1218\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0munpack_singleton\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/image-search/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2713\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2714\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2715\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2716\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2717\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/image-search/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2675\u001b[0;31m             \u001b[0mfetched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2676\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/image-search/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1437\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[1;32m   1438\u001b[0m               \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1439\u001b[0;31m               run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1440\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1441\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model = change_stage(model, lr=1e-4, stage=2)\n",
    "make_steps(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### stage3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "进一步降低lr，并重新训练第一阶段模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-11T11:05:10.068880Z",
     "start_time": "2019-04-11T11:05:07.384128Z"
    }
   },
   "outputs": [],
   "source": [
    "model = load_modelfile(model, 'ckpt_model.12-5.3285-0.8696.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-11T11:54:06.703997Z",
     "start_time": "2019-04-11T11:05:11.390550Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 13/22\n",
      "412/412 [==============================] - 305s 739ms/step - loss: 3.6397 - blue_cates_loss: 0.3294 - blue_lands_loss: 2.8943 - concate_category_loss: 0.2385 - attribute_loss: 3.9222 - blue_cates_top1: 0.6478 - blue_cates_top2: 0.8330 - blue_cates_top3: 0.9122 - blue_cates_top5: 0.9730 - val_loss: 4.3439 - val_blue_cates_loss: 1.0398 - val_blue_lands_loss: 2.7313 - val_concate_category_loss: 1.2197 - val_attribute_loss: 4.5081 - val_blue_cates_top1: 0.6147 - val_blue_cates_top2: 0.8037 - val_blue_cates_top3: 0.8916 - val_blue_cates_top5: 0.9653\n",
      "\n",
      "Epoch 00013: val_loss improved from inf to 4.34387, saving model to ../data/output/models/fashionnet/ckpt_model.13-4.3439-1.0398.h5\n",
      "Epoch 14/22\n",
      "412/412 [==============================] - 292s 709ms/step - loss: 3.4839 - blue_cates_loss: 0.3045 - blue_lands_loss: 2.7731 - concate_category_loss: 0.2091 - attribute_loss: 3.8538 - blue_cates_top1: 0.6617 - blue_cates_top2: 0.8480 - blue_cates_top3: 0.9197 - blue_cates_top5: 0.9758 - val_loss: 4.1022 - val_blue_cates_loss: 0.8233 - val_blue_lands_loss: 2.7207 - val_concate_category_loss: 1.0705 - val_attribute_loss: 4.5107 - val_blue_cates_top1: 0.6353 - val_blue_cates_top2: 0.8274 - val_blue_cates_top3: 0.9076 - val_blue_cates_top5: 0.9686\n",
      "\n",
      "Epoch 00014: val_loss improved from 4.34387 to 4.10218, saving model to ../data/output/models/fashionnet/ckpt_model.14-4.1022-0.8233.h5\n",
      "Epoch 15/22\n",
      "412/412 [==============================] - 290s 703ms/step - loss: 3.3668 - blue_cates_loss: 0.2853 - blue_lands_loss: 2.6815 - concate_category_loss: 0.1943 - attribute_loss: 3.8059 - blue_cates_top1: 0.6779 - blue_cates_top2: 0.8543 - blue_cates_top3: 0.9254 - blue_cates_top5: 0.9786 - val_loss: 4.3418 - val_blue_cates_loss: 0.9080 - val_blue_lands_loss: 2.8635 - val_concate_category_loss: 1.1586 - val_attribute_loss: 4.5451 - val_blue_cates_top1: 0.6379 - val_blue_cates_top2: 0.8254 - val_blue_cates_top3: 0.9036 - val_blue_cates_top5: 0.9688\n",
      "\n",
      "Epoch 00015: val_loss did not improve from 4.10218\n",
      "Epoch 16/22\n",
      "412/412 [==============================] - 291s 706ms/step - loss: 3.2703 - blue_cates_loss: 0.2648 - blue_lands_loss: 2.6108 - concate_category_loss: 0.1814 - attribute_loss: 3.7659 - blue_cates_top1: 0.6890 - blue_cates_top2: 0.8672 - blue_cates_top3: 0.9325 - blue_cates_top5: 0.9810 - val_loss: 4.2925 - val_blue_cates_loss: 0.9482 - val_blue_lands_loss: 2.7731 - val_concate_category_loss: 1.1686 - val_attribute_loss: 4.5432 - val_blue_cates_top1: 0.6507 - val_blue_cates_top2: 0.8374 - val_blue_cates_top3: 0.9124 - val_blue_cates_top5: 0.9705\n",
      "\n",
      "Epoch 00016: val_loss did not improve from 4.10218\n",
      "Epoch 17/22\n",
      "412/412 [==============================] - 288s 700ms/step - loss: 3.1854 - blue_cates_loss: 0.2531 - blue_lands_loss: 2.5440 - concate_category_loss: 0.1673 - attribute_loss: 3.7160 - blue_cates_top1: 0.7008 - blue_cates_top2: 0.8735 - blue_cates_top3: 0.9365 - blue_cates_top5: 0.9832 - val_loss: 4.4341 - val_blue_cates_loss: 0.9166 - val_blue_lands_loss: 2.9309 - val_concate_category_loss: 1.2697 - val_attribute_loss: 4.5965 - val_blue_cates_top1: 0.6515 - val_blue_cates_top2: 0.8353 - val_blue_cates_top3: 0.9102 - val_blue_cates_top5: 0.9690\n",
      "\n",
      "Epoch 00017: val_loss did not improve from 4.10218\n",
      "Epoch 18/22\n",
      "412/412 [==============================] - 292s 708ms/step - loss: 3.1034 - blue_cates_loss: 0.2445 - blue_lands_loss: 2.4756 - concate_category_loss: 0.1588 - attribute_loss: 3.6740 - blue_cates_top1: 0.7092 - blue_cates_top2: 0.8789 - blue_cates_top3: 0.9410 - blue_cates_top5: 0.9845 - val_loss: 4.4577 - val_blue_cates_loss: 0.9846 - val_blue_lands_loss: 2.8883 - val_concate_category_loss: 1.2171 - val_attribute_loss: 4.6313 - val_blue_cates_top1: 0.6362 - val_blue_cates_top2: 0.8279 - val_blue_cates_top3: 0.9073 - val_blue_cates_top5: 0.9692\n",
      "\n",
      "Epoch 00018: val_loss did not improve from 4.10218\n",
      "Epoch 19/22\n",
      "412/412 [==============================] - 291s 707ms/step - loss: 3.0130 - blue_cates_loss: 0.2379 - blue_lands_loss: 2.3974 - concate_category_loss: 0.1489 - attribute_loss: 3.6280 - blue_cates_top1: 0.7190 - blue_cates_top2: 0.8870 - blue_cates_top3: 0.9459 - blue_cates_top5: 0.9858 - val_loss: 4.5100 - val_blue_cates_loss: 0.9901 - val_blue_lands_loss: 2.9268 - val_concate_category_loss: 1.2208 - val_attribute_loss: 4.7105 - val_blue_cates_top1: 0.6502 - val_blue_cates_top2: 0.8383 - val_blue_cates_top3: 0.9131 - val_blue_cates_top5: 0.9687\n",
      "\n",
      "Epoch 00019: val_loss did not improve from 4.10218\n",
      "Epoch 20/22\n",
      "412/412 [==============================] - 284s 689ms/step - loss: 2.9487 - blue_cates_loss: 0.2257 - blue_lands_loss: 2.3489 - concate_category_loss: 0.1449 - attribute_loss: 3.5962 - blue_cates_top1: 0.7263 - blue_cates_top2: 0.8901 - blue_cates_top3: 0.9481 - blue_cates_top5: 0.9870 - val_loss: 4.5810 - val_blue_cates_loss: 1.0137 - val_blue_lands_loss: 2.9703 - val_concate_category_loss: 1.2507 - val_attribute_loss: 4.7196 - val_blue_cates_top1: 0.6635 - val_blue_cates_top2: 0.8417 - val_blue_cates_top3: 0.9123 - val_blue_cates_top5: 0.9704\n",
      "\n",
      "Epoch 00020: val_loss did not improve from 4.10218\n",
      "Epoch 21/22\n",
      "412/412 [==============================] - 288s 698ms/step - loss: 2.8777 - blue_cates_loss: 0.2172 - blue_lands_loss: 2.2919 - concate_category_loss: 0.1367 - attribute_loss: 3.5486 - blue_cates_top1: 0.7337 - blue_cates_top2: 0.8941 - blue_cates_top3: 0.9509 - blue_cates_top5: 0.9874 - val_loss: 4.7188 - val_blue_cates_loss: 1.0720 - val_blue_lands_loss: 3.0471 - val_concate_category_loss: 1.2197 - val_attribute_loss: 4.7771 - val_blue_cates_top1: 0.6558 - val_blue_cates_top2: 0.8420 - val_blue_cates_top3: 0.9112 - val_blue_cates_top5: 0.9681\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 4.10218\n",
      "Epoch 22/22\n",
      "412/412 [==============================] - 288s 700ms/step - loss: 2.8184 - blue_cates_loss: 0.2217 - blue_lands_loss: 2.2308 - concate_category_loss: 0.1368 - attribute_loss: 3.5223 - blue_cates_top1: 0.7424 - blue_cates_top2: 0.9007 - blue_cates_top3: 0.9534 - blue_cates_top5: 0.9889 - val_loss: 4.6209 - val_blue_cates_loss: 0.9395 - val_blue_lands_loss: 3.0715 - val_concate_category_loss: 1.2898 - val_attribute_loss: 4.8087 - val_blue_cates_top1: 0.6551 - val_blue_cates_top2: 0.8354 - val_blue_cates_top3: 0.9087 - val_blue_cates_top5: 0.9680\n",
      "\n",
      "Epoch 00022: val_loss did not improve from 4.10218\n",
      "22 3e-05\n"
     ]
    }
   ],
   "source": [
    "model = change_stage(model, lr=3e-5, stage=1)\n",
    "make_steps(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### stage4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-12T01:27:43.040772Z",
     "start_time": "2019-04-12T01:27:41.856142Z"
    }
   },
   "outputs": [],
   "source": [
    "model = load_modelfile(model, 'ckpt_model.14-4.1022-0.8233.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-12T02:16:56.703077Z",
     "start_time": "2019-04-12T01:27:51.608732Z"
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 15/24\n",
      "412/412 [==============================] - 300s 728ms/step - loss: 3.9724 - blue_cates_loss: 0.2767 - blue_lands_loss: 2.6650 - concate_category_loss: 0.1867 - attribute_loss: 3.7857 - concate_category_top1: 0.7790 - concate_category_top2: 0.9168 - concate_category_top3: 0.9625 - concate_category_top5: 0.9908 - val_loss: 5.6329 - val_blue_cates_loss: 0.8233 - val_blue_lands_loss: 2.7207 - val_concate_category_loss: 1.1039 - val_attribute_loss: 4.5290 - val_concate_category_top1: 0.6657 - val_concate_category_top2: 0.8487 - val_concate_category_top3: 0.9166 - val_concate_category_top5: 0.9677\n",
      "\n",
      "Epoch 00015: val_loss improved from inf to 5.63294, saving model to ../data/output/models/fashionnet/ckpt_model.15-5.6329-0.8233.h5\n",
      "Epoch 16/24\n",
      "412/412 [==============================] - 290s 703ms/step - loss: 3.9406 - blue_cates_loss: 0.2777 - blue_lands_loss: 2.6482 - concate_category_loss: 0.1774 - attribute_loss: 3.7632 - concate_category_top1: 0.7818 - concate_category_top2: 0.9187 - concate_category_top3: 0.9655 - concate_category_top5: 0.9922 - val_loss: 5.6402 - val_blue_cates_loss: 0.8233 - val_blue_lands_loss: 2.7207 - val_concate_category_loss: 1.0988 - val_attribute_loss: 4.5414 - val_concate_category_top1: 0.6702 - val_concate_category_top2: 0.8508 - val_concate_category_top3: 0.9179 - val_concate_category_top5: 0.9681\n",
      "\n",
      "Epoch 00016: val_loss did not improve from 5.63294\n",
      "Epoch 17/24\n",
      "412/412 [==============================] - 290s 704ms/step - loss: 3.9105 - blue_cates_loss: 0.2770 - blue_lands_loss: 2.6633 - concate_category_loss: 0.1673 - attribute_loss: 3.7431 - concate_category_top1: 0.7867 - concate_category_top2: 0.9217 - concate_category_top3: 0.9669 - concate_category_top5: 0.9923 - val_loss: 5.6700 - val_blue_cates_loss: 0.8233 - val_blue_lands_loss: 2.7207 - val_concate_category_loss: 1.1239 - val_attribute_loss: 4.5461 - val_concate_category_top1: 0.6698 - val_concate_category_top2: 0.8491 - val_concate_category_top3: 0.9164 - val_concate_category_top5: 0.9667\n",
      "\n",
      "Epoch 00017: val_loss did not improve from 5.63294\n",
      "Epoch 18/24\n",
      "412/412 [==============================] - 297s 721ms/step - loss: 3.8931 - blue_cates_loss: 0.2802 - blue_lands_loss: 2.6670 - concate_category_loss: 0.1663 - attribute_loss: 3.7268 - concate_category_top1: 0.7910 - concate_category_top2: 0.9235 - concate_category_top3: 0.9661 - concate_category_top5: 0.9925 - val_loss: 5.6925 - val_blue_cates_loss: 0.8233 - val_blue_lands_loss: 2.7207 - val_concate_category_loss: 1.1342 - val_attribute_loss: 4.5583 - val_concate_category_top1: 0.6719 - val_concate_category_top2: 0.8513 - val_concate_category_top3: 0.9186 - val_concate_category_top5: 0.9678\n",
      "\n",
      "Epoch 00018: val_loss did not improve from 5.63294\n",
      "Epoch 19/24\n",
      "412/412 [==============================] - 299s 726ms/step - loss: 3.8656 - blue_cates_loss: 0.2784 - blue_lands_loss: 2.6373 - concate_category_loss: 0.1603 - attribute_loss: 3.7053 - concate_category_top1: 0.7970 - concate_category_top2: 0.9246 - concate_category_top3: 0.9678 - concate_category_top5: 0.9935 - val_loss: 5.7681 - val_blue_cates_loss: 0.8233 - val_blue_lands_loss: 2.7207 - val_concate_category_loss: 1.1872 - val_attribute_loss: 4.5808 - val_concate_category_top1: 0.6794 - val_concate_category_top2: 0.8540 - val_concate_category_top3: 0.9200 - val_concate_category_top5: 0.9688\n",
      "\n",
      "Epoch 00019: val_loss did not improve from 5.63294\n",
      "Epoch 20/24\n",
      "412/412 [==============================] - 300s 728ms/step - loss: 3.8457 - blue_cates_loss: 0.2792 - blue_lands_loss: 2.6548 - concate_category_loss: 0.1534 - attribute_loss: 3.6923 - concate_category_top1: 0.8030 - concate_category_top2: 0.9295 - concate_category_top3: 0.9676 - concate_category_top5: 0.9932 - val_loss: 5.7291 - val_blue_cates_loss: 0.8233 - val_blue_lands_loss: 2.7207 - val_concate_category_loss: 1.1377 - val_attribute_loss: 4.5914 - val_concate_category_top1: 0.6745 - val_concate_category_top2: 0.8510 - val_concate_category_top3: 0.9177 - val_concate_category_top5: 0.9667\n",
      "\n",
      "Epoch 00020: val_loss did not improve from 5.63294\n",
      "Epoch 21/24\n",
      "412/412 [==============================] - 295s 716ms/step - loss: 3.8245 - blue_cates_loss: 0.2818 - blue_lands_loss: 2.6512 - concate_category_loss: 0.1497 - attribute_loss: 3.6748 - concate_category_top1: 0.8030 - concate_category_top2: 0.9313 - concate_category_top3: 0.9715 - concate_category_top5: 0.9933 - val_loss: 5.7719 - val_blue_cates_loss: 0.8233 - val_blue_lands_loss: 2.7207 - val_concate_category_loss: 1.1662 - val_attribute_loss: 4.6057 - val_concate_category_top1: 0.6743 - val_concate_category_top2: 0.8499 - val_concate_category_top3: 0.9189 - val_concate_category_top5: 0.9674\n",
      "\n",
      "Epoch 00021: val_loss did not improve from 5.63294\n",
      "Epoch 22/24\n",
      "412/412 [==============================] - 284s 690ms/step - loss: 3.8015 - blue_cates_loss: 0.2824 - blue_lands_loss: 2.6556 - concate_category_loss: 0.1490 - attribute_loss: 3.6524 - concate_category_top1: 0.8093 - concate_category_top2: 0.9328 - concate_category_top3: 0.9721 - concate_category_top5: 0.9944 - val_loss: 5.8351 - val_blue_cates_loss: 0.8233 - val_blue_lands_loss: 2.7207 - val_concate_category_loss: 1.2012 - val_attribute_loss: 4.6340 - val_concate_category_top1: 0.6777 - val_concate_category_top2: 0.8554 - val_concate_category_top3: 0.9185 - val_concate_category_top5: 0.9674\n",
      "\n",
      "Epoch 00022: val_loss did not improve from 5.63294\n",
      "Epoch 23/24\n",
      "412/412 [==============================] - 287s 696ms/step - loss: 3.7801 - blue_cates_loss: 0.2793 - blue_lands_loss: 2.6651 - concate_category_loss: 0.1446 - attribute_loss: 3.6354 - concate_category_top1: 0.8114 - concate_category_top2: 0.9337 - concate_category_top3: 0.9722 - concate_category_top5: 0.9933 - val_loss: 5.8826 - val_blue_cates_loss: 0.8233 - val_blue_lands_loss: 2.7207 - val_concate_category_loss: 1.2159 - val_attribute_loss: 4.6668 - val_concate_category_top1: 0.6775 - val_concate_category_top2: 0.8510 - val_concate_category_top3: 0.9170 - val_concate_category_top5: 0.9660\n",
      "\n",
      "Epoch 00023: val_loss did not improve from 5.63294\n",
      "Epoch 24/24\n",
      "412/412 [==============================] - 293s 710ms/step - loss: 3.7578 - blue_cates_loss: 0.2801 - blue_lands_loss: 2.6609 - concate_category_loss: 0.1398 - attribute_loss: 3.6181 - concate_category_top1: 0.8158 - concate_category_top2: 0.9374 - concate_category_top3: 0.9746 - concate_category_top5: 0.9945 - val_loss: 5.8526 - val_blue_cates_loss: 0.8233 - val_blue_lands_loss: 2.7207 - val_concate_category_loss: 1.2122 - val_attribute_loss: 4.6404 - val_concate_category_top1: 0.6790 - val_concate_category_top2: 0.8520 - val_concate_category_top3: 0.9179 - val_concate_category_top5: 0.9665\n",
      "\n",
      "Epoch 00024: val_loss did not improve from 5.63294\n",
      "24 1e-05\n"
     ]
    }
   ],
   "source": [
    "model = change_stage(model, lr=1e-5, stage=2)\n",
    "make_steps(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### stage5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-26T02:37:07.346391Z",
     "start_time": "2019-03-26T02:36:31.689237Z"
    }
   },
   "source": [
    "重复以上2个步骤，直到模型最终拟合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "image-search",
   "language": "python",
   "name": "image-search"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "186px"
   },
   "toc_section_display": true,
   "toc_window_display": true
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 "nbformat_minor": 2
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